library(tidyverse)
library(data.table)
library(ggh4x)
library(lme4)
library(car)
library(ggeffects)
library(doParallel)
cores <- getOption("mc.cores", detectCores()) # for parallel computation
cl <- makeCluster(cores)
registerDoParallel(cl)
data loading
dat <- fread("exp1_data_eyetracking.csv", header = TRUE)
dat <- subset(dat, dat$event == "fixation" & dat$conf != 0) # conf should be NA
dat$condition <- as.factor(dat$condition)
dat$fixItem <- factor(dat$fixItem, levels = c("target", "distractor", "dud", "other"))
dat$chosenItem <- factor(dat$chosenItem, levels = c("target", "distractor", "dud"))
dat %>% group_by(subj) %>% mutate(conf_normalized = scale(conf)) -> dat # subject-wise normalization of confidence
dat %>% mutate(q_dur_distractor = ifelse(dur_distractor > quantile(dur_distractor)[4], 0.75,
ifelse(dur_distractor <= quantile(dur_distractor)[4] & dur_distractor > quantile(dur_distractor)[3], 0.5,
ifelse(dur_distractor <= quantile(dur_distractor)[3] & dur_distractor > quantile(dur_distractor)[2], 0.25, 0)))) -> dat
dat %>% mutate(tdDurationRatio = dur_target/dur_distractor) -> dat
subject-wise fixation plot
plot1 <- foreach(i = unique(dat$subj), .packages = c("tidyverse", "ggh4x")) %dopar% {
subset(dat, dat$subj == i) %>%
ggplot() + geom_point(aes(x = x, y = y, size = dur, color = condition), alpha = 0.3) +
facet_nested(. ~ targetPos + dudPos) + ggtitle(i) %>% print()
}
plot1
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
##
## [[7]]
##
## [[8]]
##
## [[9]]
##
## [[10]]
Position-based fixation frequency
dat %>%
group_by(condition, targetPos, dudPos, fixItem, subj) %>%
summarise(n = n()) %>%
ungroup() -> freq # ungroup() is necessary for plotting dud fix data
## `summarise()` has grouped output by 'condition', 'targetPos', 'dudPos',
## 'fixItem'. You can override using the `.groups` argument.
plot2 <- foreach(i = unique(freq$condition), .packages = c("tidyverse", "ggh4x")) %dopar% {
p <- ggplot(subset(freq, freq$condition == i)) +
geom_violin(aes(x = fixItem, y = n, color = fixItem)) +
geom_point(aes(x = fixItem, y = n, color = fixItem)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
facet_nested(. ~ targetPos + dudPos) + ggtitle(i) %>% print()
}
plot2
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
All trials
dat %>%
group_by(subj, condition) %>%
mutate(n_trials = n_distinct(trial), sum_fixations = n()) %>%
group_by(fixItem, condition, subj) %>%
mutate(n_fixations = n(), fpt = sum_fixations/n_trials, cfpt = n()/n_trials) %>%
select(sum_fixations, n_fixations, n_trials, fpt, cfpt) %>%
distinct() -> fd1
## Adding missing grouping variables: `fixItem`, `condition`, `subj`
# total fixation frequency
ggplot(fd1) + geom_violin(aes(x = "", y = fpt)) + geom_point(aes(x = "", y = fpt, color = subj)) +
xlab("") + ylab("Mean fixations per trial") + facet_wrap(. ~ condition)
# condition-wise fixation frequency
ggplot(fd1) + geom_violin(aes(x = fixItem, y = cfpt)) + geom_point(aes(x = fixItem, y = cfpt)) +
theme(axis.text.x = element_text(angle = 30, hjust = 1)) + ylab("Mean fixations per trial") + facet_wrap(. ~ condition)
AOI only
# total fixation frequency (AOI only) other以外へのfixationがなかった試行は除かれる
dat %>%
filter(., fixItem != "other") %>%
group_by(subj, condition) %>%
mutate(n_trials = n_distinct(trial), sum_fixations = n()) %>%
group_by(fixItem, condition, subj) %>%
mutate(n_fixations = n(), fpt = sum_fixations/n_trials, cfpt = n()/n_trials) %>%
select(sum_fixations, n_fixations, n_trials, fpt, cfpt) %>%
distinct() -> fd2
## Adding missing grouping variables: `fixItem`, `condition`, `subj`
# total fixation frequency (AOI only)
ggplot(fd2) + geom_violin(aes(x = "", y = fpt)) + geom_point(aes(x = "", y = fpt, color = subj)) +
xlab("") + ylab("Mean fixations per trial (AOI only)") + facet_wrap(. ~ condition)
# condition-wise fixation frequency (AOI only)
ggplot(fd2) + geom_violin(aes(x = fixItem, y = cfpt)) + geom_point(aes(x = fixItem, y = cfpt)) +
theme(axis.text.x = element_text(angle = 30, hjust = 1)) + ylab("Mean fixation per trial (AOI only)") + facet_wrap(. ~ condition)
Target and distractor only
dat %>%
filter(., fixItem != "other" & fixItem != "dud") %>%
group_by(subj, condition) %>%
mutate(n_trials = n_distinct(trial), sum_fixations = n()) %>%
group_by(fixItem, condition, subj) %>%
mutate(n_fixations = n(), fpt = sum_fixations/n_trials, cfpt = n()/n_trials) %>%
select(sum_fixations, n_fixations, n_trials, fpt, cfpt) %>%
distinct() -> fd3
## Adding missing grouping variables: `fixItem`, `condition`, `subj`
# total fixation frequency
ggplot(fd3) + geom_violin(aes(x = "", y = fpt)) + geom_point(aes(x = "", y = fpt)) +
xlab("") + ylab("Mean fixations per trial (target and distractor only)") + facet_wrap(. ~ condition)
# condition-wise fixation frequency (target and distractor only)
ggplot(fd3) + geom_violin(aes(x = fixItem, y = cfpt)) + geom_point(aes(x = fixItem, y = cfpt)) +
theme(axis.text.x = element_text(angle = 30, hjust = 1)) + ylab("Mean fixations per trial (target and distractor only)") + facet_wrap(. ~ condition)
Stimulus-based fixation frequency (choice considered)
dat %>%
group_by(subj, condition, chosenItem) %>%
mutate(n_trials = n_distinct(trial), sum_fixations = n()) %>%
group_by(fixItem, condition, subj, chosenItem) %>%
mutate(n_fixations = n(), fpt = sum_fixations/n_trials, cfpt = n()/n_trials) %>%
select(sum_fixations, n_fixations, n_trials, fpt, cfpt) %>%
distinct() -> fd4
## Adding missing grouping variables: `fixItem`, `condition`, `subj`, `chosenItem`
ggplot(fd4) + geom_violin(aes(x = chosenItem, y = fpt)) + geom_point(aes(x = chosenItem, y = fpt)) +
ylab("Mean fixations per trial") + facet_wrap(. ~ condition)
ggplot(fd4) + geom_violin(aes(x = chosenItem, y = cfpt, color = fixItem)) +
geom_point(aes(x = chosenItem, y = cfpt, color = fixItem), position = position_dodge(width = 0.85)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ylim(0, 2.5) + ylab("Mean fixations per trial") + facet_wrap(. ~ condition)
## Warning: Removed 1 rows containing non-finite values (`stat_ydensity()`).
## Warning: Groups with fewer than two data points have been dropped.
## Groups with fewer than two data points have been dropped.
## Groups with fewer than two data points have been dropped.
## Groups with fewer than two data points have been dropped.
## Groups with fewer than two data points have been dropped.
## Warning: Removed 1 rows containing missing values (`geom_point()`).
Stimulus-based fixation frequency (confidence considered)
dat %>%
distinct(subj, condition, conf, trial, nFix, nFix_target) %>%
group_by(subj, condition, conf) %>%
summarise(fpt = mean(nFix), tfpt = mean(nFix_target)) %>%
ungroup(subj, condition, conf) -> fd5
## `summarise()` has grouped output by 'subj', 'condition'. You can override using
## the `.groups` argument.
# total fixations
ggplot(fd5) + geom_violin(aes(x = factor(conf), y = fpt)) + geom_point(aes(x = factor(conf), y = fpt)) +
ylab("Mean fixations per trial") + facet_wrap(. ~ condition)
# total fixations
ggplot(fd5) + geom_violin(aes(x = factor(conf), y = tfpt)) + geom_point(aes(x = factor(conf), y = tfpt)) +
ylab("Mean target fixations per trial") + facet_wrap(. ~ condition)
Stimulus-based fixation frequency (choice and confidence
considered)
dat %>%
distinct(subj, condition, chosenItem, conf, trial, nFix, nFix_target) %>%
group_by(subj, condition, chosenItem, conf) %>%
summarise(fpt = mean(nFix), tfpt = mean(nFix_target)) %>%
ungroup(subj, condition, chosenItem, conf) %>%
complete(subj, condition, chosenItem, conf) -> fd6
## `summarise()` has grouped output by 'subj', 'condition', 'chosenItem'. You can
## override using the `.groups` argument.
fd6$fpt[is.na(fd6$fpt)] <- 0
fd6$tfpt[is.na(fd6$tfpt)] <- 0
# total fixations
ggplot(fd6) + geom_violin(aes(x = factor(conf), y = fpt, color = chosenItem)) +
geom_point(aes(x = factor(conf), y = fpt, color = chosenItem), position = position_dodge(width = 0.85)) +
ylab("Mean fixations per trial") + facet_wrap(. ~ condition)
# total target fixations
ggplot(fd6) + geom_violin(aes(x = factor(conf), y = tfpt, color = chosenItem)) +
geom_point(aes(x = factor(conf), y = tfpt, color = chosenItem), position = position_dodge(width = 0.85)) +
ylab("Mean target fixations per trial") + ylim(0, 2.5) + facet_wrap(. ~ condition)
## Warning: Removed 1 rows containing non-finite values (`stat_ydensity()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
fixation dynamics
p_dat <- foreach(i = 1:7, .combine = rbind, .packages = "tidyverse") %dopar% {
dat %>% filter(event == "fixation" & countFix <= i) %>%
group_by(subj, condition) %>%
mutate(totalFix = n()) %>%
group_by(subj, fixItem, condition) %>%
mutate(fix = n(), pFix = n()/totalFix) %>%
select(subj, fixItem, fix, totalFix, pFix, condition) -> df
df$i <- i
print(distinct(df))
}
p_dat %>%
ungroup(subj, fixItem, i) %>%
complete(subj, fixItem, i) -> p_dat
p_dat$fix[is.na(p_dat$fix)] <- 0
p_dat$pFix[is.na(p_dat$pFix)] <- 0
ggplot(p_dat, aes(x = i, y = pFix, color = fixItem)) + geom_point() +
stat_summary(fun.y = "mean", geom = "line", position = position_dodge(width = .9)) +
scale_x_continuous(breaks = seq(2, 7, 1), limits = c(1.5, 7.5)) +
ylab("Cumulative fixation proportion") + facet_wrap(. ~ condition)
## Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
## ℹ Please use the `fun` argument instead.
## Warning: Removed 240 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 240 rows containing missing values (`geom_point()`).
fixation dynamics (exclude other fixations)
p_dat <- foreach(i = 1:7, .combine = rbind, .packages = "tidyverse") %dopar% {
dat %>% filter(event == "fixation" & fixItem != "other" & countFix <= i) %>%
group_by(subj, condition) %>%
mutate(totalFix = n()) %>%
group_by(subj, fixItem, condition) %>%
mutate(fix = n(), pFix = n()/totalFix) %>%
select(subj, fixItem, fix, totalFix, pFix, condition) -> df
df$i <- i
print(distinct(df))
}
p_dat %>%
ungroup(subj, fixItem, i) %>%
complete(subj, fixItem, i) -> p_dat
p_dat$fix[is.na(p_dat$fix)] <- 0
p_dat$pFix[is.na(p_dat$pFix)] <- 0
p_dat <- subset(p_dat, p_dat$fixItem != "other")
ggplot(p_dat, aes(x = i, y = pFix, color = fixItem)) + geom_point() +
stat_summary(fun.y = "mean", geom = "line") +
scale_x_continuous(breaks = seq(2, 7, 1), limits = c(1.5, 7.5)) + ylim(0, 0.7) +
xlab("countFix") + ylab("Cumulative fixation proportion") + facet_wrap(. ~ condition)
## Warning: Removed 180 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 180 rows containing missing values (`geom_point()`).
fixation dynamics (exclude other and dud fixations)
p_dat <- foreach(i = 1:7, .combine = rbind, .packages = "tidyverse") %dopar% {
dat %>% filter(event == "fixation" & fixItem != "other" & fixItem != "dud" & countFix <= i) %>%
group_by(subj, condition) %>%
mutate(totalFix = n()) %>%
group_by(subj, fixItem, condition) %>%
mutate(fix = n(), pFix = n()/totalFix) %>%
select(subj, fixItem, fix, totalFix, pFix, condition) -> df
df$i <- i
print(distinct(df))
}
p_dat %>%
ungroup(subj, fixItem, i) %>%
complete(subj, fixItem, i) -> p_dat
p_dat$fix[is.na(p_dat$fix)] <- 0
p_dat$pFix[is.na(p_dat$pFix)] <- 0
p_dat <- subset(p_dat, p_dat$fixItem != "other" & p_dat$fixItem != "dud")
ggplot(p_dat, aes(x = i, y = pFix, color = fixItem)) + geom_point() +
stat_summary(fun.y = "mean", geom = "line") +
scale_x_continuous(breaks = seq(2, 7, 1), limits = c(1.5, 7.5)) + ylim(0, 0.7) +
xlab("countFix") + ylab("Cumulative fixation proportion") + facet_wrap(. ~ condition)
## Warning: Removed 120 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 120 rows containing missing values (`geom_point()`).
ggplot(subset(p_dat, p_dat$i != 1), aes(x = as.numeric(as.character(condition)), y = pFix, color = fixItem)) + geom_point() +
stat_summary(fun.y = "mean", geom = "line") +
xlab("Condition") + ylab("Cumulative fixation proportion") + facet_wrap(. ~ i)
nFix_target, nFix_distractorの両者で反応正誤を説明
hist(dat$nFix_target)
hist(dat$nFix_distractor)
cor(dat$nFix_target, dat$nFix_distractor)
## [1] 0.2119962
# condition aggregated
ggplot(subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3),
aes(x = nFix_target, , y = corr, color = factor(nFix_distractor))) +
geom_count(position = position_dodge(width = 0.3)) +
stat_summary(fun.y = "mean", geom = "line") +
scale_x_continuous(breaks = seq(0, 3, 1), limits = c(-0.5, 3.5))
f1 <- glm(corr ~ nFix_target * nFix_distractor, family = binomial,
data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f1)
##
## Call:
## glm(formula = corr ~ nFix_target * nFix_distractor, family = binomial,
## data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <=
## 3))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5899 0.4089 0.7120 0.7120 0.9806
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.72589 0.03337 21.750 < 2e-16 ***
## nFix_target 0.86412 0.03003 28.776 < 2e-16 ***
## nFix_distractor -0.08118 0.02710 -2.995 0.00274 **
## nFix_target:nFix_distractor -0.26585 0.02059 -12.914 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 65497 on 61283 degrees of freedom
## Residual deviance: 63801 on 61280 degrees of freedom
## AIC: 63809
##
## Number of Fisher Scoring iterations: 4
Anova(f1)
## Analysis of Deviance Table (Type II tests)
##
## Response: corr
## LR Chisq Df Pr(>Chisq)
## nFix_target 1180.02 1 < 2.2e-16 ***
## nFix_distractor 675.89 1 < 2.2e-16 ***
## nFix_target:nFix_distractor 160.40 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f1, terms = c("nFix_target", "nFix_distractor")))
f2 <- glm(corr ~ nFix_target * factor(nFix_distractor), family = binomial,
data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f2)
##
## Call:
## glm(formula = corr ~ nFix_target * factor(nFix_distractor), family = binomial,
## data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <=
## 3))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7172 0.4100 0.7277 0.7277 0.9456
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.79545 0.04457 17.847 < 2e-16 ***
## nFix_target 0.95693 0.04875 19.628 < 2e-16 ***
## factor(nFix_distractor)1 -0.22238 0.05058 -4.396 1.10e-05 ***
## factor(nFix_distractor)2 -0.15117 0.06669 -2.267 0.0234 *
## factor(nFix_distractor)3 0.26621 0.12658 2.103 0.0354 *
## nFix_target:factor(nFix_distractor)1 -0.33656 0.05305 -6.344 2.23e-10 ***
## nFix_target:factor(nFix_distractor)2 -0.70130 0.05951 -11.785 < 2e-16 ***
## nFix_target:factor(nFix_distractor)3 -1.06525 0.08480 -12.562 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 65497 on 61283 degrees of freedom
## Residual deviance: 63697 on 61276 degrees of freedom
## AIC: 63713
##
## Number of Fisher Scoring iterations: 4
Anova(f2)
## Analysis of Deviance Table (Type II tests)
##
## Response: corr
## LR Chisq Df Pr(>Chisq)
## nFix_target 1177.35 1 < 2.2e-16 ***
## factor(nFix_distractor) 700.84 3 < 2.2e-16 ***
## nFix_target:factor(nFix_distractor) 239.73 3 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f2, terms = c("nFix_target", "nFix_distractor")))
# condition separated
ggplot(subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3),
aes(x = nFix_target, , y = corr, color = factor(nFix_distractor))) +
geom_count(position = position_dodge(width = 1.2)) +
stat_summary(fun.y = "mean", geom = "line") +
scale_x_continuous(breaks = seq(0, 3, 1), limits = c(-0.5, 3.5)) + facet_wrap(. ~ condition)
## Warning: `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
f3 <- glm(corr ~ nFix_target * nFix_distractor * factor(condition), family = binomial,
data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f3)
##
## Call:
## glm(formula = corr ~ nFix_target * nFix_distractor * factor(condition),
## family = binomial, data = subset(dat, dat$nFix_target <=
## 3 & dat$nFix_distractor <= 3))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4401 0.4354 0.6665 0.7131 1.1779
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 0.658479 0.099158 6.641
## nFix_target 0.887061 0.083207 10.661
## nFix_distractor -0.018923 0.073250 -0.258
## factor(condition)0.3 0.177896 0.134519 1.322
## factor(condition)0.5 0.057240 0.130903 0.437
## factor(condition)0.7 0.308028 0.130683 2.357
## factor(condition)0.85 0.147753 0.123500 1.196
## factor(condition)0.95 -0.193565 0.121881 -1.588
## nFix_target:nFix_distractor -0.287192 0.053060 -5.413
## nFix_target:factor(condition)0.3 -0.289499 0.113644 -2.547
## nFix_target:factor(condition)0.5 0.026534 0.111489 0.238
## nFix_target:factor(condition)0.7 0.092034 0.117024 0.786
## nFix_target:factor(condition)0.85 -0.000768 0.108116 -0.007
## nFix_target:factor(condition)0.95 0.034725 0.106391 0.326
## nFix_distractor:factor(condition)0.3 0.095380 0.103485 0.922
## nFix_distractor:factor(condition)0.5 0.071478 0.099036 0.722
## nFix_distractor:factor(condition)0.7 -0.303614 0.102626 -2.958
## nFix_distractor:factor(condition)0.85 -0.214016 0.096263 -2.223
## nFix_distractor:factor(condition)0.95 -0.085643 0.095616 -0.896
## nFix_target:nFix_distractor:factor(condition)0.3 0.039630 0.076673 0.517
## nFix_target:nFix_distractor:factor(condition)0.5 -0.069568 0.070854 -0.982
## nFix_target:nFix_distractor:factor(condition)0.7 0.055615 0.080393 0.692
## nFix_target:nFix_distractor:factor(condition)0.85 0.093867 0.071281 1.317
## nFix_target:nFix_distractor:factor(condition)0.95 0.013025 0.071989 0.181
## Pr(>|z|)
## (Intercept) 3.12e-11 ***
## nFix_target < 2e-16 ***
## nFix_distractor 0.79615
## factor(condition)0.3 0.18602
## factor(condition)0.5 0.66192
## factor(condition)0.7 0.01842 *
## factor(condition)0.85 0.23155
## factor(condition)0.95 0.11225
## nFix_target:nFix_distractor 6.21e-08 ***
## nFix_target:factor(condition)0.3 0.01085 *
## nFix_target:factor(condition)0.5 0.81188
## nFix_target:factor(condition)0.7 0.43160
## nFix_target:factor(condition)0.85 0.99433
## nFix_target:factor(condition)0.95 0.74413
## nFix_distractor:factor(condition)0.3 0.35669
## nFix_distractor:factor(condition)0.5 0.47045
## nFix_distractor:factor(condition)0.7 0.00309 **
## nFix_distractor:factor(condition)0.85 0.02620 *
## nFix_distractor:factor(condition)0.95 0.37042
## nFix_target:nFix_distractor:factor(condition)0.3 0.60525
## nFix_target:nFix_distractor:factor(condition)0.5 0.32617
## nFix_target:nFix_distractor:factor(condition)0.7 0.48907
## nFix_target:nFix_distractor:factor(condition)0.85 0.18788
## nFix_target:nFix_distractor:factor(condition)0.95 0.85643
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 65497 on 61283 degrees of freedom
## Residual deviance: 63544 on 61260 degrees of freedom
## AIC: 63592
##
## Number of Fisher Scoring iterations: 4
Anova(f3)
## Analysis of Deviance Table (Type II tests)
##
## Response: corr
## LR Chisq Df Pr(>Chisq)
## nFix_target 1171.00 1 < 2.2e-16 ***
## nFix_distractor 702.91 1 < 2.2e-16 ***
## factor(condition) 149.69 5 < 2.2e-16 ***
## nFix_target:nFix_distractor 157.34 1 < 2.2e-16 ***
## nFix_target:factor(condition) 62.41 5 3.850e-12 ***
## nFix_distractor:factor(condition) 61.99 5 4.721e-12 ***
## nFix_target:nFix_distractor:factor(condition) 6.69 5 0.2446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f3, terms = c("nFix_target", "nFix_distractor", "condition")))
f4 <- glm(corr ~ nFix_target * factor(nFix_distractor) * factor(condition), family = binomial,
data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f4)
##
## Call:
## glm(formula = corr ~ nFix_target * factor(nFix_distractor) *
## factor(condition), family = binomial, data = subset(dat,
## dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5447 0.4290 0.6893 0.7247 1.2932
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.41093 0.15573
## nFix_target 1.26856 0.16683
## factor(nFix_distractor)1 0.28644 0.16802
## factor(nFix_distractor)2 0.15488 0.19787
## factor(nFix_distractor)3 0.06497 0.29110
## factor(condition)0.3 0.47884 0.20177
## factor(condition)0.5 0.48312 0.19856
## factor(condition)0.7 0.68899 0.19078
## factor(condition)0.85 0.43051 0.18056
## factor(condition)0.95 0.13439 0.17913
## nFix_target:factor(nFix_distractor)1 -0.76305 0.17473
## nFix_target:factor(nFix_distractor)2 -0.80693 0.18784
## nFix_target:factor(nFix_distractor)3 -1.27376 0.21991
## nFix_target:factor(condition)0.3 -0.60679 0.21215
## nFix_target:factor(condition)0.5 -0.20906 0.21554
## nFix_target:factor(condition)0.7 -0.21967 0.21032
## nFix_target:factor(condition)0.85 -0.26929 0.19669
## nFix_target:factor(condition)0.95 -0.31501 0.19210
## factor(nFix_distractor)1:factor(condition)0.3 -0.27102 0.21968
## factor(nFix_distractor)2:factor(condition)0.3 -0.37933 0.26631
## factor(nFix_distractor)3:factor(condition)0.3 1.77629 0.47861
## factor(nFix_distractor)1:factor(condition)0.5 -0.57400 0.21699
## factor(nFix_distractor)2:factor(condition)0.5 0.06508 0.26475
## factor(nFix_distractor)3:factor(condition)0.5 0.75189 0.43028
## factor(nFix_distractor)1:factor(condition)0.7 -0.86121 0.20929
## factor(nFix_distractor)2:factor(condition)0.7 -0.56123 0.25930
## factor(nFix_distractor)3:factor(condition)0.7 -1.43292 0.48089
## factor(nFix_distractor)1:factor(condition)0.85 -0.64135 0.20008
## factor(nFix_distractor)2:factor(condition)0.85 -0.46932 0.24889
## factor(nFix_distractor)3:factor(condition)0.85 0.38594 0.49340
## factor(nFix_distractor)1:factor(condition)0.95 -0.55699 0.19718
## factor(nFix_distractor)2:factor(condition)0.95 -0.37748 0.24590
## factor(nFix_distractor)3:factor(condition)0.95 0.50057 0.44713
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3 0.44626 0.22384
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3 0.39322 0.24543
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3 -0.28195 0.32865
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5 0.33407 0.22794
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5 -0.27669 0.24614
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5 -0.16698 0.29789
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7 0.50525 0.22325
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7 0.07794 0.24298
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7 0.77350 0.37892
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85 0.51773 0.21039
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85 0.03628 0.23058
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85 0.46791 0.32060
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95 0.49770 0.20456
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95 0.11430 0.22493
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95 0.14622 0.31274
## z value Pr(>|z|)
## (Intercept) 2.639 0.008323 **
## nFix_target 7.604 2.87e-14 ***
## factor(nFix_distractor)1 1.705 0.088239 .
## factor(nFix_distractor)2 0.783 0.433773
## factor(nFix_distractor)3 0.223 0.823384
## factor(condition)0.3 2.373 0.017634 *
## factor(condition)0.5 2.433 0.014970 *
## factor(condition)0.7 3.611 0.000305 ***
## factor(condition)0.85 2.384 0.017110 *
## factor(condition)0.95 0.750 0.453124
## nFix_target:factor(nFix_distractor)1 -4.367 1.26e-05 ***
## nFix_target:factor(nFix_distractor)2 -4.296 1.74e-05 ***
## nFix_target:factor(nFix_distractor)3 -5.792 6.95e-09 ***
## nFix_target:factor(condition)0.3 -2.860 0.004234 **
## nFix_target:factor(condition)0.5 -0.970 0.332076
## nFix_target:factor(condition)0.7 -1.044 0.296283
## nFix_target:factor(condition)0.85 -1.369 0.170966
## nFix_target:factor(condition)0.95 -1.640 0.101041
## factor(nFix_distractor)1:factor(condition)0.3 -1.234 0.217311
## factor(nFix_distractor)2:factor(condition)0.3 -1.424 0.154331
## factor(nFix_distractor)3:factor(condition)0.3 3.711 0.000206 ***
## factor(nFix_distractor)1:factor(condition)0.5 -2.645 0.008161 **
## factor(nFix_distractor)2:factor(condition)0.5 0.246 0.805819
## factor(nFix_distractor)3:factor(condition)0.5 1.747 0.080560 .
## factor(nFix_distractor)1:factor(condition)0.7 -4.115 3.87e-05 ***
## factor(nFix_distractor)2:factor(condition)0.7 -2.164 0.030431 *
## factor(nFix_distractor)3:factor(condition)0.7 -2.980 0.002885 **
## factor(nFix_distractor)1:factor(condition)0.85 -3.206 0.001348 **
## factor(nFix_distractor)2:factor(condition)0.85 -1.886 0.059340 .
## factor(nFix_distractor)3:factor(condition)0.85 0.782 0.434100
## factor(nFix_distractor)1:factor(condition)0.95 -2.825 0.004732 **
## factor(nFix_distractor)2:factor(condition)0.95 -1.535 0.124760
## factor(nFix_distractor)3:factor(condition)0.95 1.120 0.262922
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3 1.994 0.046190 *
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3 1.602 0.109127
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3 -0.858 0.390947
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5 1.466 0.142757
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5 -1.124 0.260952
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5 -0.561 0.575122
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7 2.263 0.023624 *
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7 0.321 0.748404
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7 2.041 0.041222 *
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85 2.461 0.013862 *
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85 0.157 0.874968
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85 1.459 0.144435
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95 2.433 0.014971 *
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95 0.508 0.611334
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95 0.468 0.640103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 65497 on 61283 degrees of freedom
## Residual deviance: 63304 on 61236 degrees of freedom
## AIC: 63400
##
## Number of Fisher Scoring iterations: 4
Anova(f4)
## Analysis of Deviance Table (Type II tests)
##
## Response: corr
## LR Chisq Df Pr(>Chisq)
## nFix_target 1158.69 1 < 2.2e-16
## factor(nFix_distractor) 734.61 3 < 2.2e-16
## factor(condition) 158.19 5 < 2.2e-16
## nFix_target:factor(nFix_distractor) 245.73 3 < 2.2e-16
## nFix_target:factor(condition) 56.67 5 5.919e-11
## factor(nFix_distractor):factor(condition) 144.56 15 < 2.2e-16
## nFix_target:factor(nFix_distractor):factor(condition) 51.13 15 7.865e-06
##
## nFix_target ***
## factor(nFix_distractor) ***
## factor(condition) ***
## nFix_target:factor(nFix_distractor) ***
## nFix_target:factor(condition) ***
## factor(nFix_distractor):factor(condition) ***
## nFix_target:factor(nFix_distractor):factor(condition) ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f4, terms = c("nFix_target", "nFix_distractor", "condition")))
nFix_target, nFix_distractorの両者で確信度を説明
hist(dat$nFix_target)
hist(dat$nFix_distractor)
hist(dat$conf)
# condition aggregated
ggplot(subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3),
aes(x = nFix_target, , y = conf, color = factor(nFix_distractor))) +
geom_count(position = position_dodge(width = 0.3)) +
stat_summary(fun.y = "mean", geom = "line") +
scale_x_continuous(breaks = seq(0, 3, 1), limits = c(-0.5, 3.5)) + facet_wrap(. ~ chosenItem)
f1 <- glm(corr ~ nFix_target * nFix_distractor, family = binomial,
data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f1)
##
## Call:
## glm(formula = corr ~ nFix_target * nFix_distractor, family = binomial,
## data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <=
## 3))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5899 0.4089 0.7120 0.7120 0.9806
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.72589 0.03337 21.750 < 2e-16 ***
## nFix_target 0.86412 0.03003 28.776 < 2e-16 ***
## nFix_distractor -0.08118 0.02710 -2.995 0.00274 **
## nFix_target:nFix_distractor -0.26585 0.02059 -12.914 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 65497 on 61283 degrees of freedom
## Residual deviance: 63801 on 61280 degrees of freedom
## AIC: 63809
##
## Number of Fisher Scoring iterations: 4
Anova(f1)
## Analysis of Deviance Table (Type II tests)
##
## Response: corr
## LR Chisq Df Pr(>Chisq)
## nFix_target 1180.02 1 < 2.2e-16 ***
## nFix_distractor 675.89 1 < 2.2e-16 ***
## nFix_target:nFix_distractor 160.40 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f1, terms = c("nFix_target", "nFix_distractor")))
f2 <- glm(corr ~ nFix_target * factor(nFix_distractor), family = binomial,
data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f2)
##
## Call:
## glm(formula = corr ~ nFix_target * factor(nFix_distractor), family = binomial,
## data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <=
## 3))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7172 0.4100 0.7277 0.7277 0.9456
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.79545 0.04457 17.847 < 2e-16 ***
## nFix_target 0.95693 0.04875 19.628 < 2e-16 ***
## factor(nFix_distractor)1 -0.22238 0.05058 -4.396 1.10e-05 ***
## factor(nFix_distractor)2 -0.15117 0.06669 -2.267 0.0234 *
## factor(nFix_distractor)3 0.26621 0.12658 2.103 0.0354 *
## nFix_target:factor(nFix_distractor)1 -0.33656 0.05305 -6.344 2.23e-10 ***
## nFix_target:factor(nFix_distractor)2 -0.70130 0.05951 -11.785 < 2e-16 ***
## nFix_target:factor(nFix_distractor)3 -1.06525 0.08480 -12.562 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 65497 on 61283 degrees of freedom
## Residual deviance: 63697 on 61276 degrees of freedom
## AIC: 63713
##
## Number of Fisher Scoring iterations: 4
Anova(f2)
## Analysis of Deviance Table (Type II tests)
##
## Response: corr
## LR Chisq Df Pr(>Chisq)
## nFix_target 1177.35 1 < 2.2e-16 ***
## factor(nFix_distractor) 700.84 3 < 2.2e-16 ***
## nFix_target:factor(nFix_distractor) 239.73 3 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f2, terms = c("nFix_target", "nFix_distractor")))
# condition separated
ggplot(subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3),
aes(x = nFix_target, , y = corr, color = factor(nFix_distractor))) +
geom_count(position = position_dodge(width = 1.2)) +
stat_summary(fun.y = "mean", geom = "line") +
scale_x_continuous(breaks = seq(0, 3, 1), limits = c(-0.5, 3.5)) + facet_wrap(. ~ condition)
## Warning: `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
## `position_dodge()` requires non-overlapping x intervals
f3 <- glm(corr ~ nFix_target * nFix_distractor * factor(condition), family = binomial,
data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f3)
##
## Call:
## glm(formula = corr ~ nFix_target * nFix_distractor * factor(condition),
## family = binomial, data = subset(dat, dat$nFix_target <=
## 3 & dat$nFix_distractor <= 3))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4401 0.4354 0.6665 0.7131 1.1779
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 0.658479 0.099158 6.641
## nFix_target 0.887061 0.083207 10.661
## nFix_distractor -0.018923 0.073250 -0.258
## factor(condition)0.3 0.177896 0.134519 1.322
## factor(condition)0.5 0.057240 0.130903 0.437
## factor(condition)0.7 0.308028 0.130683 2.357
## factor(condition)0.85 0.147753 0.123500 1.196
## factor(condition)0.95 -0.193565 0.121881 -1.588
## nFix_target:nFix_distractor -0.287192 0.053060 -5.413
## nFix_target:factor(condition)0.3 -0.289499 0.113644 -2.547
## nFix_target:factor(condition)0.5 0.026534 0.111489 0.238
## nFix_target:factor(condition)0.7 0.092034 0.117024 0.786
## nFix_target:factor(condition)0.85 -0.000768 0.108116 -0.007
## nFix_target:factor(condition)0.95 0.034725 0.106391 0.326
## nFix_distractor:factor(condition)0.3 0.095380 0.103485 0.922
## nFix_distractor:factor(condition)0.5 0.071478 0.099036 0.722
## nFix_distractor:factor(condition)0.7 -0.303614 0.102626 -2.958
## nFix_distractor:factor(condition)0.85 -0.214016 0.096263 -2.223
## nFix_distractor:factor(condition)0.95 -0.085643 0.095616 -0.896
## nFix_target:nFix_distractor:factor(condition)0.3 0.039630 0.076673 0.517
## nFix_target:nFix_distractor:factor(condition)0.5 -0.069568 0.070854 -0.982
## nFix_target:nFix_distractor:factor(condition)0.7 0.055615 0.080393 0.692
## nFix_target:nFix_distractor:factor(condition)0.85 0.093867 0.071281 1.317
## nFix_target:nFix_distractor:factor(condition)0.95 0.013025 0.071989 0.181
## Pr(>|z|)
## (Intercept) 3.12e-11 ***
## nFix_target < 2e-16 ***
## nFix_distractor 0.79615
## factor(condition)0.3 0.18602
## factor(condition)0.5 0.66192
## factor(condition)0.7 0.01842 *
## factor(condition)0.85 0.23155
## factor(condition)0.95 0.11225
## nFix_target:nFix_distractor 6.21e-08 ***
## nFix_target:factor(condition)0.3 0.01085 *
## nFix_target:factor(condition)0.5 0.81188
## nFix_target:factor(condition)0.7 0.43160
## nFix_target:factor(condition)0.85 0.99433
## nFix_target:factor(condition)0.95 0.74413
## nFix_distractor:factor(condition)0.3 0.35669
## nFix_distractor:factor(condition)0.5 0.47045
## nFix_distractor:factor(condition)0.7 0.00309 **
## nFix_distractor:factor(condition)0.85 0.02620 *
## nFix_distractor:factor(condition)0.95 0.37042
## nFix_target:nFix_distractor:factor(condition)0.3 0.60525
## nFix_target:nFix_distractor:factor(condition)0.5 0.32617
## nFix_target:nFix_distractor:factor(condition)0.7 0.48907
## nFix_target:nFix_distractor:factor(condition)0.85 0.18788
## nFix_target:nFix_distractor:factor(condition)0.95 0.85643
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 65497 on 61283 degrees of freedom
## Residual deviance: 63544 on 61260 degrees of freedom
## AIC: 63592
##
## Number of Fisher Scoring iterations: 4
Anova(f3)
## Analysis of Deviance Table (Type II tests)
##
## Response: corr
## LR Chisq Df Pr(>Chisq)
## nFix_target 1171.00 1 < 2.2e-16 ***
## nFix_distractor 702.91 1 < 2.2e-16 ***
## factor(condition) 149.69 5 < 2.2e-16 ***
## nFix_target:nFix_distractor 157.34 1 < 2.2e-16 ***
## nFix_target:factor(condition) 62.41 5 3.850e-12 ***
## nFix_distractor:factor(condition) 61.99 5 4.721e-12 ***
## nFix_target:nFix_distractor:factor(condition) 6.69 5 0.2446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f3, terms = c("nFix_target", "nFix_distractor", "condition")))
f4 <- glm(corr ~ nFix_target * factor(nFix_distractor) * factor(condition), family = binomial,
data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
summary(f4)
##
## Call:
## glm(formula = corr ~ nFix_target * factor(nFix_distractor) *
## factor(condition), family = binomial, data = subset(dat,
## dat$nFix_target <= 3 & dat$nFix_distractor <= 3))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5447 0.4290 0.6893 0.7247 1.2932
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.41093 0.15573
## nFix_target 1.26856 0.16683
## factor(nFix_distractor)1 0.28644 0.16802
## factor(nFix_distractor)2 0.15488 0.19787
## factor(nFix_distractor)3 0.06497 0.29110
## factor(condition)0.3 0.47884 0.20177
## factor(condition)0.5 0.48312 0.19856
## factor(condition)0.7 0.68899 0.19078
## factor(condition)0.85 0.43051 0.18056
## factor(condition)0.95 0.13439 0.17913
## nFix_target:factor(nFix_distractor)1 -0.76305 0.17473
## nFix_target:factor(nFix_distractor)2 -0.80693 0.18784
## nFix_target:factor(nFix_distractor)3 -1.27376 0.21991
## nFix_target:factor(condition)0.3 -0.60679 0.21215
## nFix_target:factor(condition)0.5 -0.20906 0.21554
## nFix_target:factor(condition)0.7 -0.21967 0.21032
## nFix_target:factor(condition)0.85 -0.26929 0.19669
## nFix_target:factor(condition)0.95 -0.31501 0.19210
## factor(nFix_distractor)1:factor(condition)0.3 -0.27102 0.21968
## factor(nFix_distractor)2:factor(condition)0.3 -0.37933 0.26631
## factor(nFix_distractor)3:factor(condition)0.3 1.77629 0.47861
## factor(nFix_distractor)1:factor(condition)0.5 -0.57400 0.21699
## factor(nFix_distractor)2:factor(condition)0.5 0.06508 0.26475
## factor(nFix_distractor)3:factor(condition)0.5 0.75189 0.43028
## factor(nFix_distractor)1:factor(condition)0.7 -0.86121 0.20929
## factor(nFix_distractor)2:factor(condition)0.7 -0.56123 0.25930
## factor(nFix_distractor)3:factor(condition)0.7 -1.43292 0.48089
## factor(nFix_distractor)1:factor(condition)0.85 -0.64135 0.20008
## factor(nFix_distractor)2:factor(condition)0.85 -0.46932 0.24889
## factor(nFix_distractor)3:factor(condition)0.85 0.38594 0.49340
## factor(nFix_distractor)1:factor(condition)0.95 -0.55699 0.19718
## factor(nFix_distractor)2:factor(condition)0.95 -0.37748 0.24590
## factor(nFix_distractor)3:factor(condition)0.95 0.50057 0.44713
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3 0.44626 0.22384
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3 0.39322 0.24543
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3 -0.28195 0.32865
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5 0.33407 0.22794
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5 -0.27669 0.24614
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5 -0.16698 0.29789
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7 0.50525 0.22325
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7 0.07794 0.24298
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7 0.77350 0.37892
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85 0.51773 0.21039
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85 0.03628 0.23058
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85 0.46791 0.32060
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95 0.49770 0.20456
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95 0.11430 0.22493
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95 0.14622 0.31274
## z value Pr(>|z|)
## (Intercept) 2.639 0.008323 **
## nFix_target 7.604 2.87e-14 ***
## factor(nFix_distractor)1 1.705 0.088239 .
## factor(nFix_distractor)2 0.783 0.433773
## factor(nFix_distractor)3 0.223 0.823384
## factor(condition)0.3 2.373 0.017634 *
## factor(condition)0.5 2.433 0.014970 *
## factor(condition)0.7 3.611 0.000305 ***
## factor(condition)0.85 2.384 0.017110 *
## factor(condition)0.95 0.750 0.453124
## nFix_target:factor(nFix_distractor)1 -4.367 1.26e-05 ***
## nFix_target:factor(nFix_distractor)2 -4.296 1.74e-05 ***
## nFix_target:factor(nFix_distractor)3 -5.792 6.95e-09 ***
## nFix_target:factor(condition)0.3 -2.860 0.004234 **
## nFix_target:factor(condition)0.5 -0.970 0.332076
## nFix_target:factor(condition)0.7 -1.044 0.296283
## nFix_target:factor(condition)0.85 -1.369 0.170966
## nFix_target:factor(condition)0.95 -1.640 0.101041
## factor(nFix_distractor)1:factor(condition)0.3 -1.234 0.217311
## factor(nFix_distractor)2:factor(condition)0.3 -1.424 0.154331
## factor(nFix_distractor)3:factor(condition)0.3 3.711 0.000206 ***
## factor(nFix_distractor)1:factor(condition)0.5 -2.645 0.008161 **
## factor(nFix_distractor)2:factor(condition)0.5 0.246 0.805819
## factor(nFix_distractor)3:factor(condition)0.5 1.747 0.080560 .
## factor(nFix_distractor)1:factor(condition)0.7 -4.115 3.87e-05 ***
## factor(nFix_distractor)2:factor(condition)0.7 -2.164 0.030431 *
## factor(nFix_distractor)3:factor(condition)0.7 -2.980 0.002885 **
## factor(nFix_distractor)1:factor(condition)0.85 -3.206 0.001348 **
## factor(nFix_distractor)2:factor(condition)0.85 -1.886 0.059340 .
## factor(nFix_distractor)3:factor(condition)0.85 0.782 0.434100
## factor(nFix_distractor)1:factor(condition)0.95 -2.825 0.004732 **
## factor(nFix_distractor)2:factor(condition)0.95 -1.535 0.124760
## factor(nFix_distractor)3:factor(condition)0.95 1.120 0.262922
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3 1.994 0.046190 *
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3 1.602 0.109127
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3 -0.858 0.390947
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5 1.466 0.142757
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5 -1.124 0.260952
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5 -0.561 0.575122
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7 2.263 0.023624 *
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7 0.321 0.748404
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7 2.041 0.041222 *
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85 2.461 0.013862 *
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85 0.157 0.874968
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85 1.459 0.144435
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95 2.433 0.014971 *
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95 0.508 0.611334
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95 0.468 0.640103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 65497 on 61283 degrees of freedom
## Residual deviance: 63304 on 61236 degrees of freedom
## AIC: 63400
##
## Number of Fisher Scoring iterations: 4
Anova(f4)
## Analysis of Deviance Table (Type II tests)
##
## Response: corr
## LR Chisq Df Pr(>Chisq)
## nFix_target 1158.69 1 < 2.2e-16
## factor(nFix_distractor) 734.61 3 < 2.2e-16
## factor(condition) 158.19 5 < 2.2e-16
## nFix_target:factor(nFix_distractor) 245.73 3 < 2.2e-16
## nFix_target:factor(condition) 56.67 5 5.919e-11
## factor(nFix_distractor):factor(condition) 144.56 15 < 2.2e-16
## nFix_target:factor(nFix_distractor):factor(condition) 51.13 15 7.865e-06
##
## nFix_target ***
## factor(nFix_distractor) ***
## factor(condition) ***
## nFix_target:factor(nFix_distractor) ***
## nFix_target:factor(condition) ***
## factor(nFix_distractor):factor(condition) ***
## nFix_target:factor(nFix_distractor):factor(condition) ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f4, terms = c("nFix_target", "nFix_distractor", "condition")))
nFix_target, nFix_distractorの両者で標準化された確信度を説明
hist(dat$nFix_target)
hist(dat$nFix_distractor)
hist(dat$conf_normalized)
# condition aggregated
ggplot(subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3 & dat$subj != "sub03"), # sub03 almost always gives conf of 4
aes(x = nFix_target, , y = conf_normalized, color = factor(nFix_distractor))) +
geom_count(position = position_dodge(width = 0.3)) +
stat_summary(fun.y = "mean", geom = "line") +
scale_x_continuous(breaks = seq(0, 3, 1), limits = c(-0.5, 3.5)) +
ylim(-3, 3) + facet_wrap(. ~ chosenItem)
f5 <- lm(conf_normalized ~ nFix_target * nFix_distractor * chosenItem,
data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3 &
dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f5)
##
## Call:
## lm(formula = conf_normalized ~ nFix_target * nFix_distractor *
## chosenItem, data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <=
## 3 & dat$chosenItem != "dud" & dat$subj != "sub03"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.96575 -0.69370 0.09428 0.65711 3.04754
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.10247 0.01705 6.011
## nFix_target 0.10629 0.01363 7.800
## nFix_distractor 0.05601 0.01419 3.947
## chosenItemdistractor -0.28743 0.03345 -8.594
## nFix_target:nFix_distractor -0.11409 0.01018 -11.212
## nFix_target:chosenItemdistractor -0.20875 0.02832 -7.371
## nFix_distractor:chosenItemdistractor -0.13654 0.02761 -4.944
## nFix_target:nFix_distractor:chosenItemdistractor 0.09478 0.02004 4.729
## Pr(>|t|)
## (Intercept) 1.86e-09 ***
## nFix_target 6.29e-15 ***
## nFix_distractor 7.93e-05 ***
## chosenItemdistractor < 2e-16 ***
## nFix_target:nFix_distractor < 2e-16 ***
## nFix_target:chosenItemdistractor 1.71e-13 ***
## nFix_distractor:chosenItemdistractor 7.66e-07 ***
## nFix_target:nFix_distractor:chosenItemdistractor 2.27e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9639 on 54634 degrees of freedom
## Multiple R-squared: 0.06001, Adjusted R-squared: 0.05989
## F-statistic: 498.3 on 7 and 54634 DF, p-value: < 2.2e-16
Anova(f5)
## Anova Table (Type II tests)
##
## Response: conf_normalized
## Sum Sq Df F value Pr(>F)
## nFix_target 51 1 54.5702 1.521e-13 ***
## nFix_distractor 186 1 200.2002 < 2.2e-16 ***
## chosenItem 2605 1 2803.6099 < 2.2e-16 ***
## nFix_target:nFix_distractor 97 1 104.5923 < 2.2e-16 ***
## nFix_target:chosenItem 37 1 40.1521 2.368e-10 ***
## nFix_distractor:chosenItem 3 1 3.2241 0.07257 .
## nFix_target:nFix_distractor:chosenItem 21 1 22.3608 2.265e-06 ***
## Residuals 50766 54634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f5, terms = c("nFix_target", "nFix_distractor", "chosenItem")))
f6 <- lm(conf_normalized ~ nFix_target * factor(nFix_distractor) * chosenItem,
data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3 &
dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f6)
##
## Call:
## lm(formula = conf_normalized ~ nFix_target * factor(nFix_distractor) *
## chosenItem, data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <=
## 3 & dat$chosenItem != "dud" & dat$subj != "sub03"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.96078 -0.66786 0.08534 0.65950 3.03392
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.11530 0.02185
## nFix_target 0.08850 0.01979
## factor(nFix_distractor)1 0.03325 0.02511
## factor(nFix_distractor)2 0.12052 0.03394
## factor(nFix_distractor)3 0.15332 0.06483
## chosenItemdistractor -0.34318 0.04366
## nFix_target:factor(nFix_distractor)1 -0.08096 0.02182
## nFix_target:factor(nFix_distractor)2 -0.26097 0.02591
## nFix_target:factor(nFix_distractor)3 -0.24987 0.04149
## nFix_target:chosenItemdistractor -0.19902 0.04536
## factor(nFix_distractor)1:chosenItemdistractor -0.03788 0.04922
## factor(nFix_distractor)2:chosenItemdistractor -0.34740 0.06532
## factor(nFix_distractor)3:chosenItemdistractor -0.30281 0.12432
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor 0.05036 0.04913
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor 0.31387 0.05612
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor 0.13104 0.08165
## t value Pr(>|t|)
## (Intercept) 5.278 1.31e-07 ***
## nFix_target 4.471 7.80e-06 ***
## factor(nFix_distractor)1 1.324 0.185438
## factor(nFix_distractor)2 3.551 0.000384 ***
## factor(nFix_distractor)3 2.365 0.018040 *
## chosenItemdistractor -7.861 3.87e-15 ***
## nFix_target:factor(nFix_distractor)1 -3.710 0.000207 ***
## nFix_target:factor(nFix_distractor)2 -10.071 < 2e-16 ***
## nFix_target:factor(nFix_distractor)3 -6.022 1.73e-09 ***
## nFix_target:chosenItemdistractor -4.387 1.15e-05 ***
## factor(nFix_distractor)1:chosenItemdistractor -0.769 0.441614
## factor(nFix_distractor)2:chosenItemdistractor -5.318 1.05e-07 ***
## factor(nFix_distractor)3:chosenItemdistractor -2.436 0.014867 *
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor 1.025 0.305390
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor 5.593 2.25e-08 ***
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor 1.605 0.108519
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9634 on 54626 degrees of freedom
## Multiple R-squared: 0.06127, Adjusted R-squared: 0.06101
## F-statistic: 237.7 on 15 and 54626 DF, p-value: < 2.2e-16
Anova(f6)
## Anova Table (Type II tests)
##
## Response: conf_normalized
## Sum Sq Df F value Pr(>F)
## nFix_target 50 1 53.7707 2.284e-13
## factor(nFix_distractor) 224 3 80.3469 < 2.2e-16
## chosenItem 2600 1 2801.5923 < 2.2e-16
## nFix_target:factor(nFix_distractor) 87 3 31.0789 < 2.2e-16
## nFix_target:chosenItem 38 1 40.5688 1.913e-10
## factor(nFix_distractor):chosenItem 12 3 4.1484 0.006007
## nFix_target:factor(nFix_distractor):chosenItem 50 3 17.9418 1.238e-11
## Residuals 50698 54626
##
## nFix_target ***
## factor(nFix_distractor) ***
## chosenItem ***
## nFix_target:factor(nFix_distractor) ***
## nFix_target:chosenItem ***
## factor(nFix_distractor):chosenItem **
## nFix_target:factor(nFix_distractor):chosenItem ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f6, terms = c("nFix_target", "nFix_distractor", "chosenItem")))
# condition separated
ggplot(subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3 &
dat$chosenItem != "dud" & dat$subj != "sub03"), # sub03 almost always gives conf of 4
aes(x = nFix_target, , y = conf_normalized, color = factor(nFix_distractor))) +
geom_count(position = position_dodge(width = 0.3)) +
stat_summary(fun.y = "mean", geom = "line") +
scale_x_continuous(breaks = seq(0, 3, 1), limits = c(-0.5, 3.5)) +
ylim(-3, 3) + facet_nested(. ~ chosenItem + condition)
f7 <- lm(conf_normalized ~ nFix_target * nFix_distractor * chosenItem * factor(condition),
data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3 &
dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f7)
##
## Call:
## lm(formula = conf_normalized ~ nFix_target * nFix_distractor *
## chosenItem * factor(condition), data = subset(dat, dat$nFix_target <=
## 3 & dat$nFix_distractor <= 3 & dat$chosenItem != "dud" &
## dat$subj != "sub03"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1010 -0.6931 0.1139 0.7333 3.3163
##
## Coefficients:
## Estimate
## (Intercept) 0.0752960
## nFix_target 0.1913408
## nFix_distractor 0.0608421
## chosenItemdistractor -0.3104762
## factor(condition)0.3 0.3251223
## factor(condition)0.5 0.2776974
## factor(condition)0.7 0.1232229
## factor(condition)0.85 -0.0779113
## factor(condition)0.95 -0.2993420
## nFix_target:nFix_distractor -0.1781547
## nFix_target:chosenItemdistractor -0.3944236
## nFix_distractor:chosenItemdistractor -0.0930693
## nFix_target:factor(condition)0.3 -0.2165153
## nFix_target:factor(condition)0.5 -0.2086613
## nFix_target:factor(condition)0.7 -0.1034811
## nFix_target:factor(condition)0.85 -0.0547068
## nFix_target:factor(condition)0.95 0.0153728
## nFix_distractor:factor(condition)0.3 -0.0104797
## nFix_distractor:factor(condition)0.5 -0.0434750
## nFix_distractor:factor(condition)0.7 0.0001316
## nFix_distractor:factor(condition)0.85 -0.0716915
## nFix_distractor:factor(condition)0.95 -0.0709066
## chosenItemdistractor:factor(condition)0.3 0.0511489
## chosenItemdistractor:factor(condition)0.5 -0.0756553
## chosenItemdistractor:factor(condition)0.7 0.1099306
## chosenItemdistractor:factor(condition)0.85 -0.1536403
## chosenItemdistractor:factor(condition)0.95 0.1240766
## nFix_target:nFix_distractor:chosenItemdistractor 0.1372286
## nFix_target:nFix_distractor:factor(condition)0.3 0.0965452
## nFix_target:nFix_distractor:factor(condition)0.5 0.1277425
## nFix_target:nFix_distractor:factor(condition)0.7 0.0407039
## nFix_target:nFix_distractor:factor(condition)0.85 0.1053057
## nFix_target:nFix_distractor:factor(condition)0.95 0.0507342
## nFix_target:chosenItemdistractor:factor(condition)0.3 0.3294910
## nFix_target:chosenItemdistractor:factor(condition)0.5 0.2720565
## nFix_target:chosenItemdistractor:factor(condition)0.7 0.1935701
## nFix_target:chosenItemdistractor:factor(condition)0.85 0.2433877
## nFix_target:chosenItemdistractor:factor(condition)0.95 0.0011849
## nFix_distractor:chosenItemdistractor:factor(condition)0.3 -0.0841418
## nFix_distractor:chosenItemdistractor:factor(condition)0.5 -0.0361155
## nFix_distractor:chosenItemdistractor:factor(condition)0.7 -0.1542726
## nFix_distractor:chosenItemdistractor:factor(condition)0.85 0.1214649
## nFix_distractor:chosenItemdistractor:factor(condition)0.95 0.0177608
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.3 -0.1111535
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.5 -0.0594830
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.7 0.0539932
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.85 -0.1343595
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.95 0.0165684
## Std. Error
## (Intercept) 0.0487084
## nFix_target 0.0363066
## nFix_distractor 0.0371688
## chosenItemdistractor 0.0909827
## factor(condition)0.3 0.0665190
## factor(condition)0.5 0.0644115
## factor(condition)0.7 0.0629589
## factor(condition)0.85 0.0610640
## factor(condition)0.95 0.0620366
## nFix_target:nFix_distractor 0.0253124
## nFix_target:chosenItemdistractor 0.0737198
## nFix_distractor:chosenItemdistractor 0.0695153
## nFix_target:factor(condition)0.3 0.0514115
## nFix_target:factor(condition)0.5 0.0490580
## nFix_target:factor(condition)0.7 0.0492718
## nFix_target:factor(condition)0.85 0.0468097
## nFix_target:factor(condition)0.95 0.0481968
## nFix_distractor:factor(condition)0.3 0.0522441
## nFix_distractor:factor(condition)0.5 0.0500810
## nFix_distractor:factor(condition)0.7 0.0513809
## nFix_distractor:factor(condition)0.85 0.0489418
## nFix_distractor:factor(condition)0.95 0.0504289
## chosenItemdistractor:factor(condition)0.3 0.1240920
## chosenItemdistractor:factor(condition)0.5 0.1222557
## chosenItemdistractor:factor(condition)0.7 0.1202176
## chosenItemdistractor:factor(condition)0.85 0.1172860
## chosenItemdistractor:factor(condition)0.95 0.1214964
## nFix_target:nFix_distractor:chosenItemdistractor 0.0491510
## nFix_target:nFix_distractor:factor(condition)0.3 0.0374508
## nFix_target:nFix_distractor:factor(condition)0.5 0.0343778
## nFix_target:nFix_distractor:factor(condition)0.7 0.0368516
## nFix_target:nFix_distractor:factor(condition)0.85 0.0334941
## nFix_target:nFix_distractor:factor(condition)0.95 0.0357016
## nFix_target:chosenItemdistractor:factor(condition)0.3 0.1002732
## nFix_target:chosenItemdistractor:factor(condition)0.5 0.0989884
## nFix_target:chosenItemdistractor:factor(condition)0.7 0.1033299
## nFix_target:chosenItemdistractor:factor(condition)0.85 0.0995152
## nFix_target:chosenItemdistractor:factor(condition)0.95 0.1014336
## nFix_distractor:chosenItemdistractor:factor(condition)0.3 0.0978450
## nFix_distractor:chosenItemdistractor:factor(condition)0.5 0.0963152
## nFix_distractor:chosenItemdistractor:factor(condition)0.7 0.0971070
## nFix_distractor:chosenItemdistractor:factor(condition)0.85 0.0944060
## nFix_distractor:chosenItemdistractor:factor(condition)0.95 0.0972364
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.3 0.0695927
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.5 0.0660706
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.7 0.0738962
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.85 0.0688887
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.95 0.0705294
## t value
## (Intercept) 1.546
## nFix_target 5.270
## nFix_distractor 1.637
## chosenItemdistractor -3.412
## factor(condition)0.3 4.888
## factor(condition)0.5 4.311
## factor(condition)0.7 1.957
## factor(condition)0.85 -1.276
## factor(condition)0.95 -4.825
## nFix_target:nFix_distractor -7.038
## nFix_target:chosenItemdistractor -5.350
## nFix_distractor:chosenItemdistractor -1.339
## nFix_target:factor(condition)0.3 -4.211
## nFix_target:factor(condition)0.5 -4.253
## nFix_target:factor(condition)0.7 -2.100
## nFix_target:factor(condition)0.85 -1.169
## nFix_target:factor(condition)0.95 0.319
## nFix_distractor:factor(condition)0.3 -0.201
## nFix_distractor:factor(condition)0.5 -0.868
## nFix_distractor:factor(condition)0.7 0.003
## nFix_distractor:factor(condition)0.85 -1.465
## nFix_distractor:factor(condition)0.95 -1.406
## chosenItemdistractor:factor(condition)0.3 0.412
## chosenItemdistractor:factor(condition)0.5 -0.619
## chosenItemdistractor:factor(condition)0.7 0.914
## chosenItemdistractor:factor(condition)0.85 -1.310
## chosenItemdistractor:factor(condition)0.95 1.021
## nFix_target:nFix_distractor:chosenItemdistractor 2.792
## nFix_target:nFix_distractor:factor(condition)0.3 2.578
## nFix_target:nFix_distractor:factor(condition)0.5 3.716
## nFix_target:nFix_distractor:factor(condition)0.7 1.105
## nFix_target:nFix_distractor:factor(condition)0.85 3.144
## nFix_target:nFix_distractor:factor(condition)0.95 1.421
## nFix_target:chosenItemdistractor:factor(condition)0.3 3.286
## nFix_target:chosenItemdistractor:factor(condition)0.5 2.748
## nFix_target:chosenItemdistractor:factor(condition)0.7 1.873
## nFix_target:chosenItemdistractor:factor(condition)0.85 2.446
## nFix_target:chosenItemdistractor:factor(condition)0.95 0.012
## nFix_distractor:chosenItemdistractor:factor(condition)0.3 -0.860
## nFix_distractor:chosenItemdistractor:factor(condition)0.5 -0.375
## nFix_distractor:chosenItemdistractor:factor(condition)0.7 -1.589
## nFix_distractor:chosenItemdistractor:factor(condition)0.85 1.287
## nFix_distractor:chosenItemdistractor:factor(condition)0.95 0.183
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.3 -1.597
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.5 -0.900
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.7 0.731
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.85 -1.950
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.95 0.235
## Pr(>|t|)
## (Intercept) 0.122146
## nFix_target 1.37e-07
## nFix_distractor 0.101654
## chosenItemdistractor 0.000644
## factor(condition)0.3 1.02e-06
## factor(condition)0.5 1.63e-05
## factor(condition)0.7 0.050329
## factor(condition)0.85 0.201998
## factor(condition)0.95 1.40e-06
## nFix_target:nFix_distractor 1.97e-12
## nFix_target:chosenItemdistractor 8.82e-08
## nFix_distractor:chosenItemdistractor 0.180631
## nFix_target:factor(condition)0.3 2.54e-05
## nFix_target:factor(condition)0.5 2.11e-05
## nFix_target:factor(condition)0.7 0.035715
## nFix_target:factor(condition)0.85 0.242528
## nFix_target:factor(condition)0.95 0.749759
## nFix_distractor:factor(condition)0.3 0.841020
## nFix_distractor:factor(condition)0.5 0.385347
## nFix_distractor:factor(condition)0.7 0.997957
## nFix_distractor:factor(condition)0.85 0.142973
## nFix_distractor:factor(condition)0.95 0.159709
## chosenItemdistractor:factor(condition)0.3 0.680205
## chosenItemdistractor:factor(condition)0.5 0.536032
## chosenItemdistractor:factor(condition)0.7 0.360495
## chosenItemdistractor:factor(condition)0.85 0.190214
## chosenItemdistractor:factor(condition)0.95 0.307147
## nFix_target:nFix_distractor:chosenItemdistractor 0.005240
## nFix_target:nFix_distractor:factor(condition)0.3 0.009942
## nFix_target:nFix_distractor:factor(condition)0.5 0.000203
## nFix_target:nFix_distractor:factor(condition)0.7 0.269366
## nFix_target:nFix_distractor:factor(condition)0.85 0.001667
## nFix_target:nFix_distractor:factor(condition)0.95 0.155305
## nFix_target:chosenItemdistractor:factor(condition)0.3 0.001017
## nFix_target:chosenItemdistractor:factor(condition)0.5 0.005991
## nFix_target:chosenItemdistractor:factor(condition)0.7 0.061029
## nFix_target:chosenItemdistractor:factor(condition)0.85 0.014459
## nFix_target:chosenItemdistractor:factor(condition)0.95 0.990679
## nFix_distractor:chosenItemdistractor:factor(condition)0.3 0.389820
## nFix_distractor:chosenItemdistractor:factor(condition)0.5 0.707683
## nFix_distractor:chosenItemdistractor:factor(condition)0.7 0.112137
## nFix_distractor:chosenItemdistractor:factor(condition)0.85 0.198231
## nFix_distractor:chosenItemdistractor:factor(condition)0.95 0.855069
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.3 0.110227
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.5 0.367968
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.7 0.464988
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.85 0.051135
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.95 0.814275
##
## (Intercept)
## nFix_target ***
## nFix_distractor
## chosenItemdistractor ***
## factor(condition)0.3 ***
## factor(condition)0.5 ***
## factor(condition)0.7 .
## factor(condition)0.85
## factor(condition)0.95 ***
## nFix_target:nFix_distractor ***
## nFix_target:chosenItemdistractor ***
## nFix_distractor:chosenItemdistractor
## nFix_target:factor(condition)0.3 ***
## nFix_target:factor(condition)0.5 ***
## nFix_target:factor(condition)0.7 *
## nFix_target:factor(condition)0.85
## nFix_target:factor(condition)0.95
## nFix_distractor:factor(condition)0.3
## nFix_distractor:factor(condition)0.5
## nFix_distractor:factor(condition)0.7
## nFix_distractor:factor(condition)0.85
## nFix_distractor:factor(condition)0.95
## chosenItemdistractor:factor(condition)0.3
## chosenItemdistractor:factor(condition)0.5
## chosenItemdistractor:factor(condition)0.7
## chosenItemdistractor:factor(condition)0.85
## chosenItemdistractor:factor(condition)0.95
## nFix_target:nFix_distractor:chosenItemdistractor **
## nFix_target:nFix_distractor:factor(condition)0.3 **
## nFix_target:nFix_distractor:factor(condition)0.5 ***
## nFix_target:nFix_distractor:factor(condition)0.7
## nFix_target:nFix_distractor:factor(condition)0.85 **
## nFix_target:nFix_distractor:factor(condition)0.95
## nFix_target:chosenItemdistractor:factor(condition)0.3 **
## nFix_target:chosenItemdistractor:factor(condition)0.5 **
## nFix_target:chosenItemdistractor:factor(condition)0.7 .
## nFix_target:chosenItemdistractor:factor(condition)0.85 *
## nFix_target:chosenItemdistractor:factor(condition)0.95
## nFix_distractor:chosenItemdistractor:factor(condition)0.3
## nFix_distractor:chosenItemdistractor:factor(condition)0.5
## nFix_distractor:chosenItemdistractor:factor(condition)0.7
## nFix_distractor:chosenItemdistractor:factor(condition)0.85
## nFix_distractor:chosenItemdistractor:factor(condition)0.95
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.3
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.5
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.7
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.85 .
## nFix_target:nFix_distractor:chosenItemdistractor:factor(condition)0.95
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9489 on 54594 degrees of freedom
## Multiple R-squared: 0.08988, Adjusted R-squared: 0.08909
## F-statistic: 114.7 on 47 and 54594 DF, p-value: < 2.2e-16
Anova(f7)
## Anova Table (Type II tests)
##
## Response: conf_normalized
## Sum Sq Df F value
## nFix_target 58 1 64.5764
## nFix_distractor 253 1 280.9254
## chosenItem 2599 1 2886.3850
## factor(condition) 1399 5 310.6717
## nFix_target:nFix_distractor 77 1 85.6076
## nFix_target:chosenItem 42 1 46.2333
## nFix_distractor:chosenItem 0 1 0.0391
## nFix_target:factor(condition) 59 5 13.0120
## nFix_distractor:factor(condition) 28 5 6.1230
## chosenItem:factor(condition) 43 5 9.4889
## nFix_target:nFix_distractor:chosenItem 19 1 21.5590
## nFix_target:nFix_distractor:factor(condition) 14 5 3.0520
## nFix_target:chosenItem:factor(condition) 36 5 8.1054
## nFix_distractor:chosenItem:factor(condition) 29 5 6.4248
## nFix_target:nFix_distractor:chosenItem:factor(condition) 10 5 2.1748
## Residuals 49153 54594
## Pr(>F)
## nFix_target 9.471e-16 ***
## nFix_distractor < 2.2e-16 ***
## chosenItem < 2.2e-16 ***
## factor(condition) < 2.2e-16 ***
## nFix_target:nFix_distractor < 2.2e-16 ***
## nFix_target:chosenItem 1.061e-11 ***
## nFix_distractor:chosenItem 0.843216
## nFix_target:factor(condition) 1.108e-12 ***
## nFix_distractor:factor(condition) 1.120e-05 ***
## chosenItem:factor(condition) 4.654e-09 ***
## nFix_target:nFix_distractor:chosenItem 3.439e-06 ***
## nFix_target:nFix_distractor:factor(condition) 0.009315 **
## nFix_target:chosenItem:factor(condition) 1.177e-07 ***
## nFix_distractor:chosenItem:factor(condition) 5.637e-06 ***
## nFix_target:nFix_distractor:chosenItem:factor(condition) 0.053948 .
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f7, terms = c("nFix_target", "nFix_distractor", "chosenItem", "condition")))
f8 <- lm(conf_normalized ~ nFix_target * factor(nFix_distractor) * chosenItem * factor(condition),
data = subset(dat, dat$nFix_target <= 3 & dat$nFix_distractor <= 3 &
dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f8)
##
## Call:
## lm(formula = conf_normalized ~ nFix_target * factor(nFix_distractor) *
## chosenItem * factor(condition), data = subset(dat, dat$nFix_target <=
## 3 & dat$nFix_distractor <= 3 & dat$chosenItem != "dud" &
## dat$subj != "sub03"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1037 -0.6837 0.0979 0.7374 3.3339
##
## Coefficients:
## Estimate
## (Intercept) 0.013880
## nFix_target 0.224227
## factor(nFix_distractor)1 0.156638
## factor(nFix_distractor)2 0.086153
## factor(nFix_distractor)3 0.388551
## chosenItemdistractor -0.114684
## factor(condition)0.3 0.379474
## factor(condition)0.5 0.330954
## factor(condition)0.7 0.291485
## factor(condition)0.85 -0.008700
## factor(condition)0.95 -0.231756
## nFix_target:factor(nFix_distractor)1 -0.227801
## nFix_target:factor(nFix_distractor)2 -0.345138
## nFix_target:factor(nFix_distractor)3 -0.632537
## nFix_target:chosenItemdistractor -0.653058
## factor(nFix_distractor)1:chosenItemdistractor -0.310632
## factor(nFix_distractor)2:chosenItemdistractor -0.574102
## factor(nFix_distractor)3:chosenItemdistractor -0.230412
## nFix_target:factor(condition)0.3 -0.219729
## nFix_target:factor(condition)0.5 -0.222327
## nFix_target:factor(condition)0.7 -0.255858
## nFix_target:factor(condition)0.85 -0.096152
## nFix_target:factor(condition)0.95 -0.030914
## factor(nFix_distractor)1:factor(condition)0.3 -0.133505
## factor(nFix_distractor)2:factor(condition)0.3 0.241369
## factor(nFix_distractor)3:factor(condition)0.3 -0.814479
## factor(nFix_distractor)1:factor(condition)0.5 -0.120937
## factor(nFix_distractor)2:factor(condition)0.5 -0.167436
## factor(nFix_distractor)3:factor(condition)0.5 0.060577
## factor(nFix_distractor)1:factor(condition)0.7 -0.270748
## factor(nFix_distractor)2:factor(condition)0.7 0.166134
## factor(nFix_distractor)3:factor(condition)0.7 -0.704050
## factor(nFix_distractor)1:factor(condition)0.85 -0.181347
## factor(nFix_distractor)2:factor(condition)0.85 -0.183849
## factor(nFix_distractor)3:factor(condition)0.85 0.198280
## factor(nFix_distractor)1:factor(condition)0.95 -0.176575
## factor(nFix_distractor)2:factor(condition)0.95 -0.085037
## factor(nFix_distractor)3:factor(condition)0.95 -0.558574
## chosenItemdistractor:factor(condition)0.3 -0.086200
## chosenItemdistractor:factor(condition)0.5 -0.365645
## chosenItemdistractor:factor(condition)0.7 -0.193289
## chosenItemdistractor:factor(condition)0.85 -0.553865
## chosenItemdistractor:factor(condition)0.95 -0.027863
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor 0.406096
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor 0.728874
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor 0.473406
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3 0.146061
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3 -0.057417
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3 0.922019
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5 0.154381
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5 0.262101
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5 0.340447
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7 0.263120
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7 0.069194
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7 0.432169
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85 0.182931
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85 0.148118
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85 0.289837
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95 0.122103
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95 0.073883
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95 0.354767
## nFix_target:chosenItemdistractor:factor(condition)0.3 0.461829
## nFix_target:chosenItemdistractor:factor(condition)0.5 0.565701
## nFix_target:chosenItemdistractor:factor(condition)0.7 0.468366
## nFix_target:chosenItemdistractor:factor(condition)0.85 0.704065
## nFix_target:chosenItemdistractor:factor(condition)0.95 0.215548
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3 0.090408
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3 -0.003854
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3 -0.134253
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5 0.303967
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5 0.453952
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5 -0.376023
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7 0.241931
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7 -0.009917
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7 -0.033059
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85 0.676994
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85 0.662522
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85 -0.055161
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95 0.194953
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95 0.267001
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95 0.375746
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3 -0.265984
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3 -0.344746
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3 -0.745450
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5 -0.365642
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5 -0.657525
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5 -0.114782
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7 -0.273971
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7 -0.238246
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7 -0.116207
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85 -0.721380
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85 -0.742785
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85 -0.433151
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95 -0.233827
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95 -0.219209
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95 -0.363323
## Std. Error
## (Intercept) 0.071069
## nFix_target 0.061351
## factor(nFix_distractor)1 0.077590
## factor(nFix_distractor)2 0.091027
## factor(nFix_distractor)3 0.158264
## chosenItemdistractor 0.139875
## factor(condition)0.3 0.093947
## factor(condition)0.5 0.091100
## factor(condition)0.7 0.085821
## factor(condition)0.85 0.083946
## factor(condition)0.95 0.084904
## nFix_target:factor(nFix_distractor)1 0.065277
## nFix_target:factor(nFix_distractor)2 0.070897
## nFix_target:factor(nFix_distractor)3 0.102498
## nFix_target:chosenItemdistractor 0.152040
## factor(nFix_distractor)1:chosenItemdistractor 0.151476
## factor(nFix_distractor)2:chosenItemdistractor 0.185971
## factor(nFix_distractor)3:chosenItemdistractor 0.274720
## nFix_target:factor(condition)0.3 0.083056
## nFix_target:factor(condition)0.5 0.080258
## nFix_target:factor(condition)0.7 0.075960
## nFix_target:factor(condition)0.85 0.073448
## nFix_target:factor(condition)0.95 0.074392
## factor(nFix_distractor)1:factor(condition)0.3 0.103383
## factor(nFix_distractor)2:factor(condition)0.3 0.126437
## factor(nFix_distractor)3:factor(condition)0.3 0.220953
## factor(nFix_distractor)1:factor(condition)0.5 0.100848
## factor(nFix_distractor)2:factor(condition)0.5 0.123977
## factor(nFix_distractor)3:factor(condition)0.5 0.212273
## factor(nFix_distractor)1:factor(condition)0.7 0.095759
## factor(nFix_distractor)2:factor(condition)0.7 0.121199
## factor(nFix_distractor)3:factor(condition)0.7 0.272520
## factor(nFix_distractor)1:factor(condition)0.85 0.094355
## factor(nFix_distractor)2:factor(condition)0.85 0.121311
## factor(nFix_distractor)3:factor(condition)0.85 0.224550
## factor(nFix_distractor)1:factor(condition)0.95 0.095090
## factor(nFix_distractor)2:factor(condition)0.95 0.121398
## factor(nFix_distractor)3:factor(condition)0.95 0.239291
## chosenItemdistractor:factor(condition)0.3 0.179339
## chosenItemdistractor:factor(condition)0.5 0.176609
## chosenItemdistractor:factor(condition)0.7 0.170552
## chosenItemdistractor:factor(condition)0.85 0.165992
## chosenItemdistractor:factor(condition)0.95 0.175102
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor 0.158930
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor 0.175707
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor 0.205425
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3 0.088803
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3 0.099673
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3 0.152128
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5 0.086213
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5 0.096489
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5 0.135326
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7 0.082048
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7 0.092864
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7 0.191686
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85 0.080024
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85 0.092842
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85 0.134419
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95 0.080690
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95 0.092376
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95 0.155625
## nFix_target:chosenItemdistractor:factor(condition)0.3 0.186571
## nFix_target:chosenItemdistractor:factor(condition)0.5 0.187549
## nFix_target:chosenItemdistractor:factor(condition)0.7 0.185461
## nFix_target:chosenItemdistractor:factor(condition)0.85 0.179388
## nFix_target:chosenItemdistractor:factor(condition)0.95 0.187518
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3 0.196624
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3 0.247310
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3 0.424169
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5 0.193977
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5 0.245278
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5 0.408879
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7 0.187542
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7 0.240477
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7 0.470384
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85 0.184152
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85 0.235972
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85 0.475908
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95 0.192138
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95 0.244049
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95 0.430445
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3 0.197171
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3 0.222538
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3 0.287262
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5 0.198562
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5 0.220738
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5 0.273356
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7 0.196442
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7 0.219450
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7 0.368015
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85 0.191337
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85 0.214888
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85 0.305750
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95 0.198280
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95 0.221801
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95 0.299339
## t value
## (Intercept) 0.195
## nFix_target 3.655
## factor(nFix_distractor)1 2.019
## factor(nFix_distractor)2 0.946
## factor(nFix_distractor)3 2.455
## chosenItemdistractor -0.820
## factor(condition)0.3 4.039
## factor(condition)0.5 3.633
## factor(condition)0.7 3.396
## factor(condition)0.85 -0.104
## factor(condition)0.95 -2.730
## nFix_target:factor(nFix_distractor)1 -3.490
## nFix_target:factor(nFix_distractor)2 -4.868
## nFix_target:factor(nFix_distractor)3 -6.171
## nFix_target:chosenItemdistractor -4.295
## factor(nFix_distractor)1:chosenItemdistractor -2.051
## factor(nFix_distractor)2:chosenItemdistractor -3.087
## factor(nFix_distractor)3:chosenItemdistractor -0.839
## nFix_target:factor(condition)0.3 -2.646
## nFix_target:factor(condition)0.5 -2.770
## nFix_target:factor(condition)0.7 -3.368
## nFix_target:factor(condition)0.85 -1.309
## nFix_target:factor(condition)0.95 -0.416
## factor(nFix_distractor)1:factor(condition)0.3 -1.291
## factor(nFix_distractor)2:factor(condition)0.3 1.909
## factor(nFix_distractor)3:factor(condition)0.3 -3.686
## factor(nFix_distractor)1:factor(condition)0.5 -1.199
## factor(nFix_distractor)2:factor(condition)0.5 -1.351
## factor(nFix_distractor)3:factor(condition)0.5 0.285
## factor(nFix_distractor)1:factor(condition)0.7 -2.827
## factor(nFix_distractor)2:factor(condition)0.7 1.371
## factor(nFix_distractor)3:factor(condition)0.7 -2.583
## factor(nFix_distractor)1:factor(condition)0.85 -1.922
## factor(nFix_distractor)2:factor(condition)0.85 -1.516
## factor(nFix_distractor)3:factor(condition)0.85 0.883
## factor(nFix_distractor)1:factor(condition)0.95 -1.857
## factor(nFix_distractor)2:factor(condition)0.95 -0.700
## factor(nFix_distractor)3:factor(condition)0.95 -2.334
## chosenItemdistractor:factor(condition)0.3 -0.481
## chosenItemdistractor:factor(condition)0.5 -2.070
## chosenItemdistractor:factor(condition)0.7 -1.133
## chosenItemdistractor:factor(condition)0.85 -3.337
## chosenItemdistractor:factor(condition)0.95 -0.159
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor 2.555
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor 4.148
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor 2.305
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3 1.645
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3 -0.576
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3 6.061
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5 1.791
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5 2.716
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5 2.516
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7 3.207
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7 0.745
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7 2.255
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85 2.286
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85 1.595
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85 2.156
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95 1.513
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95 0.800
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95 2.280
## nFix_target:chosenItemdistractor:factor(condition)0.3 2.475
## nFix_target:chosenItemdistractor:factor(condition)0.5 3.016
## nFix_target:chosenItemdistractor:factor(condition)0.7 2.525
## nFix_target:chosenItemdistractor:factor(condition)0.85 3.925
## nFix_target:chosenItemdistractor:factor(condition)0.95 1.149
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3 0.460
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3 -0.016
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3 -0.317
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5 1.567
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5 1.851
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5 -0.920
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7 1.290
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7 -0.041
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7 -0.070
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85 3.676
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85 2.808
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85 -0.116
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95 1.015
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95 1.094
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95 0.873
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3 -1.349
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3 -1.549
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3 -2.595
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5 -1.841
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5 -2.979
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5 -0.420
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7 -1.395
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7 -1.086
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7 -0.316
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85 -3.770
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85 -3.457
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85 -1.417
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95 -1.179
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95 -0.988
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95 -1.214
## Pr(>|t|)
## (Intercept) 0.845154
## nFix_target 0.000258
## factor(nFix_distractor)1 0.043514
## factor(nFix_distractor)2 0.343921
## factor(nFix_distractor)3 0.014089
## chosenItemdistractor 0.412276
## factor(condition)0.3 5.37e-05
## factor(condition)0.5 0.000281
## factor(condition)0.7 0.000683
## factor(condition)0.85 0.917455
## factor(condition)0.95 0.006343
## nFix_target:factor(nFix_distractor)1 0.000484
## nFix_target:factor(nFix_distractor)2 1.13e-06
## nFix_target:factor(nFix_distractor)3 6.82e-10
## nFix_target:chosenItemdistractor 1.75e-05
## factor(nFix_distractor)1:chosenItemdistractor 0.040301
## factor(nFix_distractor)2:chosenItemdistractor 0.002023
## factor(nFix_distractor)3:chosenItemdistractor 0.401631
## nFix_target:factor(condition)0.3 0.008158
## nFix_target:factor(condition)0.5 0.005605
## nFix_target:factor(condition)0.7 0.000757
## nFix_target:factor(condition)0.85 0.190498
## nFix_target:factor(condition)0.95 0.677740
## factor(nFix_distractor)1:factor(condition)0.3 0.196582
## factor(nFix_distractor)2:factor(condition)0.3 0.056267
## factor(nFix_distractor)3:factor(condition)0.3 0.000228
## factor(nFix_distractor)1:factor(condition)0.5 0.230453
## factor(nFix_distractor)2:factor(condition)0.5 0.176848
## factor(nFix_distractor)3:factor(condition)0.5 0.775361
## factor(nFix_distractor)1:factor(condition)0.7 0.004695
## factor(nFix_distractor)2:factor(condition)0.7 0.170456
## factor(nFix_distractor)3:factor(condition)0.7 0.009784
## factor(nFix_distractor)1:factor(condition)0.85 0.054614
## factor(nFix_distractor)2:factor(condition)0.85 0.129648
## factor(nFix_distractor)3:factor(condition)0.85 0.377234
## factor(nFix_distractor)1:factor(condition)0.95 0.063327
## factor(nFix_distractor)2:factor(condition)0.95 0.483629
## factor(nFix_distractor)3:factor(condition)0.95 0.019584
## chosenItemdistractor:factor(condition)0.3 0.630765
## chosenItemdistractor:factor(condition)0.5 0.038423
## chosenItemdistractor:factor(condition)0.7 0.257087
## chosenItemdistractor:factor(condition)0.85 0.000848
## chosenItemdistractor:factor(condition)0.95 0.873572
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor 0.010616
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor 3.36e-05
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor 0.021197
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3 0.100021
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3 0.564579
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3 1.36e-09
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5 0.073346
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5 0.006602
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5 0.011880
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7 0.001342
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7 0.456207
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7 0.024164
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85 0.022261
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85 0.110633
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85 0.031071
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95 0.130225
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95 0.423827
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95 0.022634
## nFix_target:chosenItemdistractor:factor(condition)0.3 0.013313
## nFix_target:chosenItemdistractor:factor(condition)0.5 0.002560
## nFix_target:chosenItemdistractor:factor(condition)0.7 0.011559
## nFix_target:chosenItemdistractor:factor(condition)0.85 8.69e-05
## nFix_target:chosenItemdistractor:factor(condition)0.95 0.250363
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3 0.645660
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3 0.987568
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3 0.751618
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5 0.117115
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5 0.064209
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5 0.357763
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7 0.197051
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7 0.967104
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7 0.943970
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85 0.000237
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85 0.004993
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85 0.907726
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95 0.310276
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95 0.273939
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95 0.382708
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3 0.177341
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3 0.121350
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3 0.009461
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5 0.065561
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5 0.002895
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5 0.674559
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7 0.163121
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7 0.277639
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7 0.752181
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85 0.000163
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85 0.000547
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85 0.156581
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95 0.238295
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95 0.323002
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95 0.224849
##
## (Intercept)
## nFix_target ***
## factor(nFix_distractor)1 *
## factor(nFix_distractor)2
## factor(nFix_distractor)3 *
## chosenItemdistractor
## factor(condition)0.3 ***
## factor(condition)0.5 ***
## factor(condition)0.7 ***
## factor(condition)0.85
## factor(condition)0.95 **
## nFix_target:factor(nFix_distractor)1 ***
## nFix_target:factor(nFix_distractor)2 ***
## nFix_target:factor(nFix_distractor)3 ***
## nFix_target:chosenItemdistractor ***
## factor(nFix_distractor)1:chosenItemdistractor *
## factor(nFix_distractor)2:chosenItemdistractor **
## factor(nFix_distractor)3:chosenItemdistractor
## nFix_target:factor(condition)0.3 **
## nFix_target:factor(condition)0.5 **
## nFix_target:factor(condition)0.7 ***
## nFix_target:factor(condition)0.85
## nFix_target:factor(condition)0.95
## factor(nFix_distractor)1:factor(condition)0.3
## factor(nFix_distractor)2:factor(condition)0.3 .
## factor(nFix_distractor)3:factor(condition)0.3 ***
## factor(nFix_distractor)1:factor(condition)0.5
## factor(nFix_distractor)2:factor(condition)0.5
## factor(nFix_distractor)3:factor(condition)0.5
## factor(nFix_distractor)1:factor(condition)0.7 **
## factor(nFix_distractor)2:factor(condition)0.7
## factor(nFix_distractor)3:factor(condition)0.7 **
## factor(nFix_distractor)1:factor(condition)0.85 .
## factor(nFix_distractor)2:factor(condition)0.85
## factor(nFix_distractor)3:factor(condition)0.85
## factor(nFix_distractor)1:factor(condition)0.95 .
## factor(nFix_distractor)2:factor(condition)0.95
## factor(nFix_distractor)3:factor(condition)0.95 *
## chosenItemdistractor:factor(condition)0.3
## chosenItemdistractor:factor(condition)0.5 *
## chosenItemdistractor:factor(condition)0.7
## chosenItemdistractor:factor(condition)0.85 ***
## chosenItemdistractor:factor(condition)0.95
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor *
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor ***
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor *
## nFix_target:factor(nFix_distractor)1:factor(condition)0.3
## nFix_target:factor(nFix_distractor)2:factor(condition)0.3
## nFix_target:factor(nFix_distractor)3:factor(condition)0.3 ***
## nFix_target:factor(nFix_distractor)1:factor(condition)0.5 .
## nFix_target:factor(nFix_distractor)2:factor(condition)0.5 **
## nFix_target:factor(nFix_distractor)3:factor(condition)0.5 *
## nFix_target:factor(nFix_distractor)1:factor(condition)0.7 **
## nFix_target:factor(nFix_distractor)2:factor(condition)0.7
## nFix_target:factor(nFix_distractor)3:factor(condition)0.7 *
## nFix_target:factor(nFix_distractor)1:factor(condition)0.85 *
## nFix_target:factor(nFix_distractor)2:factor(condition)0.85
## nFix_target:factor(nFix_distractor)3:factor(condition)0.85 *
## nFix_target:factor(nFix_distractor)1:factor(condition)0.95
## nFix_target:factor(nFix_distractor)2:factor(condition)0.95
## nFix_target:factor(nFix_distractor)3:factor(condition)0.95 *
## nFix_target:chosenItemdistractor:factor(condition)0.3 *
## nFix_target:chosenItemdistractor:factor(condition)0.5 **
## nFix_target:chosenItemdistractor:factor(condition)0.7 *
## nFix_target:chosenItemdistractor:factor(condition)0.85 ***
## nFix_target:chosenItemdistractor:factor(condition)0.95
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5 .
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85 ***
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85 **
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85
## factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95
## factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95
## factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.3
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.3
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.3 **
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.5 .
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.5 **
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.5
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.7
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.7
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.7
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.85 ***
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.85 ***
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.85
## nFix_target:factor(nFix_distractor)1:chosenItemdistractor:factor(condition)0.95
## nFix_target:factor(nFix_distractor)2:chosenItemdistractor:factor(condition)0.95
## nFix_target:factor(nFix_distractor)3:chosenItemdistractor:factor(condition)0.95
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9468 on 54546 degrees of freedom
## Multiple R-squared: 0.09465, Adjusted R-squared: 0.09308
## F-statistic: 60.03 on 95 and 54546 DF, p-value: < 2.2e-16
Anova(f8)
## Anova Table (Type II tests)
##
## Response: conf_normalized
## Sum Sq Df
## nFix_target 60 1
## factor(nFix_distractor) 280 3
## chosenItem 2559 1
## factor(condition) 1400 5
## nFix_target:factor(nFix_distractor) 84 3
## nFix_target:chosenItem 40 1
## factor(nFix_distractor):chosenItem 4 3
## nFix_target:factor(condition) 53 5
## factor(nFix_distractor):factor(condition) 115 15
## chosenItem:factor(condition) 40 5
## nFix_target:factor(nFix_distractor):chosenItem 48 3
## nFix_target:factor(nFix_distractor):factor(condition) 74 15
## nFix_target:chosenItem:factor(condition) 33 5
## factor(nFix_distractor):chosenItem:factor(condition) 60 15
## nFix_target:factor(nFix_distractor):chosenItem:factor(condition) 39 15
## Residuals 48895 54546
## F value
## nFix_target 67.3104
## factor(nFix_distractor) 104.2530
## chosenItem 2854.8848
## factor(condition) 312.3903
## nFix_target:factor(nFix_distractor) 31.1949
## nFix_target:chosenItem 44.9883
## factor(nFix_distractor):chosenItem 1.5052
## nFix_target:factor(condition) 11.8591
## factor(nFix_distractor):factor(condition) 8.5224
## chosenItem:factor(condition) 8.9407
## nFix_target:factor(nFix_distractor):chosenItem 17.9054
## nFix_target:factor(nFix_distractor):factor(condition) 5.4674
## nFix_target:chosenItem:factor(condition) 7.2520
## factor(nFix_distractor):chosenItem:factor(condition) 4.4514
## nFix_target:factor(nFix_distractor):chosenItem:factor(condition) 2.8669
## Residuals
## Pr(>F)
## nFix_target 2.370e-16 ***
## factor(nFix_distractor) < 2.2e-16 ***
## chosenItem < 2.2e-16 ***
## factor(condition) < 2.2e-16 ***
## nFix_target:factor(nFix_distractor) < 2.2e-16 ***
## nFix_target:chosenItem 2.001e-11 ***
## factor(nFix_distractor):chosenItem 0.2109129
## nFix_target:factor(condition) 1.724e-11 ***
## factor(nFix_distractor):factor(condition) < 2.2e-16 ***
## chosenItem:factor(condition) 1.680e-08 ***
## nFix_target:factor(nFix_distractor):chosenItem 1.306e-11 ***
## nFix_target:factor(nFix_distractor):factor(condition) 3.056e-11 ***
## nFix_target:chosenItem:factor(condition) 8.469e-07 ***
## factor(nFix_distractor):chosenItem:factor(condition) 1.691e-08 ***
## nFix_target:factor(nFix_distractor):chosenItem:factor(condition) 0.0001579 ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f8, terms = c("nFix_target", "nFix_distractor", "chosenItem", "condition")))
dur_target, dur_distractorの両者で反応正誤を説明
hist(dat$dur_target)
hist(dat$dur_distractor)
plot(dat$dur_target, dat$dur_distractor)
cor(dat$dur_target, dat$dur_distractor)
## [1] 0.4845666
# condition aggregated
ggplot(dat, aes(x = dur_target, y = corr, color = factor(q_dur_distractor))) +
geom_count(alpha = 0.5) + stat_smooth() +
scale_x_continuous(breaks = seq(0, 1, 0.25), limits = c(0, 1))
## Warning: Removed 61 rows containing non-finite values (`stat_sum()`).
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
## Warning: Removed 61 rows containing non-finite values (`stat_smooth()`).
f9 <- glm(corr ~ dur_target * dur_distractor, family = binomial, data = dat)
summary(f9)
##
## Call:
## glm(formula = corr ~ dur_target * dur_distractor, family = binomial,
## data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7958 0.4568 0.6509 0.7500 1.3507
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.82597 0.02468 33.473 < 2e-16 ***
## dur_target 3.71125 0.10864 34.160 < 2e-16 ***
## dur_distractor -0.81541 0.10136 -8.045 8.63e-16 ***
## dur_target:dur_distractor -3.98799 0.28047 -14.219 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 66331 on 62108 degrees of freedom
## Residual deviance: 64494 on 62105 degrees of freedom
## AIC: 64502
##
## Number of Fisher Scoring iterations: 4
Anova(f9)
## Analysis of Deviance Table (Type II tests)
##
## Response: corr
## LR Chisq Df Pr(>Chisq)
## dur_target 1450.77 1 < 2.2e-16 ***
## dur_distractor 941.83 1 < 2.2e-16 ***
## dur_target:dur_distractor 195.03 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f9, terms = c("dur_target", "dur_distractor")))
## Data were 'prettified'. Consider using `terms="dur_target [all]"` to get
## smooth plots.
f10 <- glm(corr ~ dur_target * factor(q_dur_distractor), data = dat)
summary(f10)
##
## Call:
## glm(formula = corr ~ dur_target * factor(q_dur_distractor), data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.10543 0.07347 0.18839 0.25983 0.36223
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.762431 0.005312 143.518 < 2e-16
## dur_target 0.388316 0.021598 17.979 < 2e-16
## factor(q_dur_distractor)0.25 -0.026849 0.008012 -3.351 0.000806
## factor(q_dur_distractor)0.5 -0.115438 0.007831 -14.741 < 2e-16
## factor(q_dur_distractor)0.75 -0.124663 0.008041 -15.504 < 2e-16
## dur_target:factor(q_dur_distractor)0.25 -0.090884 0.029929 -3.037 0.002393
## dur_target:factor(q_dur_distractor)0.5 -0.015596 0.028614 -0.545 0.585728
## dur_target:factor(q_dur_distractor)0.75 -0.146424 0.027646 -5.296 1.19e-07
##
## (Intercept) ***
## dur_target ***
## factor(q_dur_distractor)0.25 ***
## factor(q_dur_distractor)0.5 ***
## factor(q_dur_distractor)0.75 ***
## dur_target:factor(q_dur_distractor)0.25 **
## dur_target:factor(q_dur_distractor)0.5
## dur_target:factor(q_dur_distractor)0.75 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1689875)
##
## Null deviance: 10853 on 62108 degrees of freedom
## Residual deviance: 10494 on 62101 degrees of freedom
## AIC: 65842
##
## Number of Fisher Scoring iterations: 2
Anova(f10)
## Analysis of Deviance Table (Type II tests)
##
## Response: corr
## LR Chisq Df Pr(>Chisq)
## dur_target 1080.61 1 < 2.2e-16 ***
## factor(q_dur_distractor) 1397.03 3 < 2.2e-16 ***
## dur_target:factor(q_dur_distractor) 39.53 3 1.339e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f10, terms = c("dur_target", "q_dur_distractor")))
# condition separated
ggplot(dat, aes(x = dur_target, y = corr, color = factor(q_dur_distractor))) +
geom_count(alpha = 0.5) + stat_smooth() +
scale_x_continuous(breaks = seq(0, 0.6, 0.2), limits = c(0, 0.6)) + facet_wrap(. ~ condition)
## Warning: Removed 2611 rows containing non-finite values (`stat_sum()`).
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
## Warning: Removed 2611 rows containing non-finite values (`stat_smooth()`).
f11 <- glm(corr ~ dur_target * dur_distractor * factor(condition), family = binomial, data = dat)
summary(f11)
##
## Call:
## glm(formula = corr ~ dur_target * dur_distractor * factor(condition),
## family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7880 0.4227 0.6510 0.7414 1.4485
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 0.84176 0.06771 12.433
## dur_target 2.65817 0.25995 10.226
## dur_distractor 0.01684 0.24333 0.069
## factor(condition)0.3 0.01274 0.09133 0.139
## factor(condition)0.5 0.03939 0.09258 0.425
## factor(condition)0.7 0.16433 0.09315 1.764
## factor(condition)0.85 0.01185 0.08946 0.132
## factor(condition)0.95 -0.23963 0.08772 -2.732
## dur_target:dur_distractor -3.54188 0.61348 -5.773
## dur_target:factor(condition)0.3 -0.13800 0.36424 -0.379
## dur_target:factor(condition)0.5 0.95942 0.38310 2.504
## dur_target:factor(condition)0.7 2.02282 0.40638 4.978
## dur_target:factor(condition)0.85 2.08693 0.37794 5.522
## dur_target:factor(condition)0.95 1.69559 0.36644 4.627
## dur_distractor:factor(condition)0.3 0.53667 0.34039 1.577
## dur_distractor:factor(condition)0.5 -0.54852 0.35672 -1.538
## dur_distractor:factor(condition)0.7 -2.19732 0.37488 -5.861
## dur_distractor:factor(condition)0.85 -1.69818 0.35276 -4.814
## dur_distractor:factor(condition)0.95 -1.58404 0.34817 -4.550
## dur_target:dur_distractor:factor(condition)0.3 -1.04099 0.85955 -1.211
## dur_target:dur_distractor:factor(condition)0.5 -0.71756 0.94609 -0.758
## dur_target:dur_distractor:factor(condition)0.7 0.46608 1.07119 0.435
## dur_target:dur_distractor:factor(condition)0.85 -1.26941 0.98389 -1.290
## dur_target:dur_distractor:factor(condition)0.95 -0.51354 0.99204 -0.518
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## dur_target < 2e-16 ***
## dur_distractor 0.9448
## factor(condition)0.3 0.8891
## factor(condition)0.5 0.6705
## factor(condition)0.7 0.0777 .
## factor(condition)0.85 0.8946
## factor(condition)0.95 0.0063 **
## dur_target:dur_distractor 7.77e-09 ***
## dur_target:factor(condition)0.3 0.7048
## dur_target:factor(condition)0.5 0.0123 *
## dur_target:factor(condition)0.7 6.44e-07 ***
## dur_target:factor(condition)0.85 3.36e-08 ***
## dur_target:factor(condition)0.95 3.71e-06 ***
## dur_distractor:factor(condition)0.3 0.1149
## dur_distractor:factor(condition)0.5 0.1241
## dur_distractor:factor(condition)0.7 4.59e-09 ***
## dur_distractor:factor(condition)0.85 1.48e-06 ***
## dur_distractor:factor(condition)0.95 5.38e-06 ***
## dur_target:dur_distractor:factor(condition)0.3 0.2259
## dur_target:dur_distractor:factor(condition)0.5 0.4482
## dur_target:dur_distractor:factor(condition)0.7 0.6635
## dur_target:dur_distractor:factor(condition)0.85 0.1970
## dur_target:dur_distractor:factor(condition)0.95 0.6047
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 66331 on 62108 degrees of freedom
## Residual deviance: 64064 on 62085 degrees of freedom
## AIC: 64112
##
## Number of Fisher Scoring iterations: 4
Anova(f11)
## Analysis of Deviance Table (Type II tests)
##
## Response: corr
## LR Chisq Df Pr(>Chisq)
## dur_target 1438.75 1 <2e-16 ***
## dur_distractor 940.85 1 <2e-16 ***
## factor(condition) 163.00 5 <2e-16 ***
## dur_target:dur_distractor 195.48 1 <2e-16 ***
## dur_target:factor(condition) 174.43 5 <2e-16 ***
## dur_distractor:factor(condition) 218.19 5 <2e-16 ***
## dur_target:dur_distractor:factor(condition) 3.75 5 0.5854
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f11, terms = c("dur_target", "dur_distractor", "condition")))
## Data were 'prettified'. Consider using `terms="dur_target [all]"` to get
## smooth plots.
f12 <- glm(corr ~ dur_target * factor(q_dur_distractor) * factor(condition), data = dat)
summary(f12)
##
## Call:
## glm(formula = corr ~ dur_target * factor(q_dur_distractor) *
## factor(condition), data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1449 0.0653 0.1871 0.2560 0.4893
##
## Coefficients:
## Estimate
## (Intercept) 0.767981
## dur_target 0.290692
## factor(q_dur_distractor)0.25 -0.017594
## factor(q_dur_distractor)0.5 -0.080322
## factor(q_dur_distractor)0.75 -0.056066
## factor(condition)0.3 0.016987
## factor(condition)0.5 0.007693
## factor(condition)0.7 0.038352
## factor(condition)0.85 -0.003807
## factor(condition)0.95 -0.067367
## dur_target:factor(q_dur_distractor)0.25 -0.118833
## dur_target:factor(q_dur_distractor)0.5 -0.016212
## dur_target:factor(q_dur_distractor)0.75 -0.189773
## dur_target:factor(condition)0.3 -0.068680
## dur_target:factor(condition)0.5 0.111667
## dur_target:factor(condition)0.7 0.081390
## dur_target:factor(condition)0.85 0.140284
## dur_target:factor(condition)0.95 0.202478
## factor(q_dur_distractor)0.25:factor(condition)0.3 -0.013091
## factor(q_dur_distractor)0.5:factor(condition)0.3 -0.032889
## factor(q_dur_distractor)0.75:factor(condition)0.3 0.003923
## factor(q_dur_distractor)0.25:factor(condition)0.5 0.021556
## factor(q_dur_distractor)0.5:factor(condition)0.5 -0.017999
## factor(q_dur_distractor)0.75:factor(condition)0.5 -0.078283
## factor(q_dur_distractor)0.25:factor(condition)0.7 -0.042500
## factor(q_dur_distractor)0.5:factor(condition)0.7 -0.084823
## factor(q_dur_distractor)0.75:factor(condition)0.7 -0.181827
## factor(q_dur_distractor)0.25:factor(condition)0.85 -0.017631
## factor(q_dur_distractor)0.5:factor(condition)0.85 -0.088208
## factor(q_dur_distractor)0.75:factor(condition)0.85 -0.089538
## factor(q_dur_distractor)0.25:factor(condition)0.95 -0.033068
## factor(q_dur_distractor)0.5:factor(condition)0.95 -0.015152
## factor(q_dur_distractor)0.75:factor(condition)0.95 -0.133848
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3 0.086707
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3 0.170993
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3 -0.033405
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5 -0.104934
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5 -0.128850
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5 0.121468
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7 0.141663
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7 0.123292
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7 0.238216
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85 0.070444
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85 0.101571
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85 -0.004519
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95 0.126462
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95 -0.136963
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95 0.136790
## Std. Error
## (Intercept) 0.017081
## dur_target 0.069478
## factor(q_dur_distractor)0.25 0.023364
## factor(q_dur_distractor)0.5 0.021810
## factor(q_dur_distractor)0.75 0.021786
## factor(condition)0.3 0.022428
## factor(condition)0.5 0.021763
## factor(condition)0.7 0.021192
## factor(condition)0.85 0.020645
## factor(condition)0.95 0.020387
## dur_target:factor(q_dur_distractor)0.25 0.085597
## dur_target:factor(q_dur_distractor)0.5 0.080255
## dur_target:factor(q_dur_distractor)0.75 0.078348
## dur_target:factor(condition)0.3 0.094088
## dur_target:factor(condition)0.5 0.089734
## dur_target:factor(condition)0.7 0.087712
## dur_target:factor(condition)0.85 0.082671
## dur_target:factor(condition)0.95 0.081802
## factor(q_dur_distractor)0.25:factor(condition)0.3 0.031199
## factor(q_dur_distractor)0.5:factor(condition)0.3 0.030108
## factor(q_dur_distractor)0.75:factor(condition)0.3 0.029657
## factor(q_dur_distractor)0.25:factor(condition)0.5 0.030764
## factor(q_dur_distractor)0.5:factor(condition)0.5 0.029148
## factor(q_dur_distractor)0.75:factor(condition)0.5 0.029539
## factor(q_dur_distractor)0.25:factor(condition)0.7 0.030067
## factor(q_dur_distractor)0.5:factor(condition)0.7 0.028627
## factor(q_dur_distractor)0.75:factor(condition)0.7 0.029661
## factor(q_dur_distractor)0.25:factor(condition)0.85 0.029778
## factor(q_dur_distractor)0.5:factor(condition)0.85 0.028780
## factor(q_dur_distractor)0.75:factor(condition)0.85 0.029344
## factor(q_dur_distractor)0.25:factor(condition)0.95 0.029547
## factor(q_dur_distractor)0.5:factor(condition)0.95 0.028238
## factor(q_dur_distractor)0.75:factor(condition)0.95 0.028893
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3 0.117564
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3 0.113663
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3 0.107724
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5 0.115212
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5 0.107537
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5 0.105484
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7 0.113424
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7 0.106263
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7 0.105998
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85 0.109084
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85 0.105630
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85 0.102661
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95 0.108222
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95 0.103790
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95 0.100917
## t value Pr(>|t|)
## (Intercept) 44.962 < 2e-16
## dur_target 4.184 2.87e-05
## factor(q_dur_distractor)0.25 -0.753 0.451433
## factor(q_dur_distractor)0.5 -3.683 0.000231
## factor(q_dur_distractor)0.75 -2.573 0.010071
## factor(condition)0.3 0.757 0.448808
## factor(condition)0.5 0.353 0.723719
## factor(condition)0.7 1.810 0.070336
## factor(condition)0.85 -0.184 0.853690
## factor(condition)0.95 -3.304 0.000952
## dur_target:factor(q_dur_distractor)0.25 -1.388 0.165053
## dur_target:factor(q_dur_distractor)0.5 -0.202 0.839909
## dur_target:factor(q_dur_distractor)0.75 -2.422 0.015431
## dur_target:factor(condition)0.3 -0.730 0.465419
## dur_target:factor(condition)0.5 1.244 0.213347
## dur_target:factor(condition)0.7 0.928 0.353448
## dur_target:factor(condition)0.85 1.697 0.089721
## dur_target:factor(condition)0.95 2.475 0.013317
## factor(q_dur_distractor)0.25:factor(condition)0.3 -0.420 0.674775
## factor(q_dur_distractor)0.5:factor(condition)0.3 -1.092 0.274669
## factor(q_dur_distractor)0.75:factor(condition)0.3 0.132 0.894778
## factor(q_dur_distractor)0.25:factor(condition)0.5 0.701 0.483496
## factor(q_dur_distractor)0.5:factor(condition)0.5 -0.618 0.536906
## factor(q_dur_distractor)0.75:factor(condition)0.5 -2.650 0.008047
## factor(q_dur_distractor)0.25:factor(condition)0.7 -1.414 0.157511
## factor(q_dur_distractor)0.5:factor(condition)0.7 -2.963 0.003047
## factor(q_dur_distractor)0.75:factor(condition)0.7 -6.130 8.83e-10
## factor(q_dur_distractor)0.25:factor(condition)0.85 -0.592 0.553787
## factor(q_dur_distractor)0.5:factor(condition)0.85 -3.065 0.002178
## factor(q_dur_distractor)0.75:factor(condition)0.85 -3.051 0.002279
## factor(q_dur_distractor)0.25:factor(condition)0.95 -1.119 0.263073
## factor(q_dur_distractor)0.5:factor(condition)0.95 -0.537 0.591561
## factor(q_dur_distractor)0.75:factor(condition)0.95 -4.632 3.62e-06
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3 0.738 0.460805
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3 1.504 0.132488
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3 -0.310 0.756487
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5 -0.911 0.362409
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5 -1.198 0.230846
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5 1.152 0.249521
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7 1.249 0.211683
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7 1.160 0.245949
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7 2.247 0.024620
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85 0.646 0.518427
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85 0.962 0.336270
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85 -0.044 0.964890
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95 1.169 0.242595
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95 -1.320 0.186969
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95 1.355 0.175274
##
## (Intercept) ***
## dur_target ***
## factor(q_dur_distractor)0.25
## factor(q_dur_distractor)0.5 ***
## factor(q_dur_distractor)0.75 *
## factor(condition)0.3
## factor(condition)0.5
## factor(condition)0.7 .
## factor(condition)0.85
## factor(condition)0.95 ***
## dur_target:factor(q_dur_distractor)0.25
## dur_target:factor(q_dur_distractor)0.5
## dur_target:factor(q_dur_distractor)0.75 *
## dur_target:factor(condition)0.3
## dur_target:factor(condition)0.5
## dur_target:factor(condition)0.7
## dur_target:factor(condition)0.85 .
## dur_target:factor(condition)0.95 *
## factor(q_dur_distractor)0.25:factor(condition)0.3
## factor(q_dur_distractor)0.5:factor(condition)0.3
## factor(q_dur_distractor)0.75:factor(condition)0.3
## factor(q_dur_distractor)0.25:factor(condition)0.5
## factor(q_dur_distractor)0.5:factor(condition)0.5
## factor(q_dur_distractor)0.75:factor(condition)0.5 **
## factor(q_dur_distractor)0.25:factor(condition)0.7
## factor(q_dur_distractor)0.5:factor(condition)0.7 **
## factor(q_dur_distractor)0.75:factor(condition)0.7 ***
## factor(q_dur_distractor)0.25:factor(condition)0.85
## factor(q_dur_distractor)0.5:factor(condition)0.85 **
## factor(q_dur_distractor)0.75:factor(condition)0.85 **
## factor(q_dur_distractor)0.25:factor(condition)0.95
## factor(q_dur_distractor)0.5:factor(condition)0.95
## factor(q_dur_distractor)0.75:factor(condition)0.95 ***
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7 *
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.167809)
##
## Null deviance: 10853 on 62108 degrees of freedom
## Residual deviance: 10414 on 62061 degrees of freedom
## AIC: 65448
##
## Number of Fisher Scoring iterations: 2
Anova(f12)
## Analysis of Deviance Table (Type II tests)
##
## Response: corr
## LR Chisq Df Pr(>Chisq)
## dur_target 1046.09 1 < 2.2e-16
## factor(q_dur_distractor) 1444.03 3 < 2.2e-16
## factor(condition) 200.48 5 < 2.2e-16
## dur_target:factor(q_dur_distractor) 28.73 3 2.546e-06
## dur_target:factor(condition) 114.60 5 < 2.2e-16
## factor(q_dur_distractor):factor(condition) 134.59 15 < 2.2e-16
## dur_target:factor(q_dur_distractor):factor(condition) 58.92 15 3.868e-07
##
## dur_target ***
## factor(q_dur_distractor) ***
## factor(condition) ***
## dur_target:factor(q_dur_distractor) ***
## dur_target:factor(condition) ***
## factor(q_dur_distractor):factor(condition) ***
## dur_target:factor(q_dur_distractor):factor(condition) ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f12, terms = c("dur_target", "q_dur_distractor", "condition")))
dur_target, dur_distractorの両者で標準化された確信度を説明
hist(dat$dur_target)
hist(dat$dur_distractor)
hist(dat$conf_normalized)
# condition aggregated
ggplot(subset(dat, dat$conf_normalized > -3), aes(x = dur_target, y = conf_normalized, color = factor(q_dur_distractor))) +
geom_count(alpha = 0.5) + stat_smooth(size = 1.2) +
scale_x_continuous(breaks = seq(0, 0.6, 0.2), limits = c(0, 0.6)) + ylim(-3, 3) + facet_wrap(. ~ chosenItem)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## Warning: Removed 2611 rows containing non-finite values (`stat_sum()`).
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
## Warning: Removed 2611 rows containing non-finite values (`stat_smooth()`).
f13 <- lm(conf_normalized ~ dur_target * dur_distractor * chosenItem,
data = subset(dat, dat$conf_normalized > -3 &
dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f13)
##
## Call:
## lm(formula = conf_normalized ~ dur_target * dur_distractor *
## chosenItem, data = subset(dat, dat$conf_normalized > -3 &
## dat$chosenItem != "dud" & dat$subj != "sub03"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9657 -0.6660 0.1154 0.6418 3.1615
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.16434 0.01275 12.888
## dur_target 0.22178 0.04789 4.631
## dur_distractor 0.07545 0.05235 1.441
## chosenItemdistractor -0.27698 0.02515 -11.013
## dur_target:dur_distractor -1.45513 0.13931 -10.445
## dur_target:chosenItemdistractor -1.17246 0.10255 -11.433
## dur_distractor:chosenItemdistractor -0.70288 0.10229 -6.872
## dur_target:dur_distractor:chosenItemdistractor 2.33020 0.26518 8.787
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## dur_target 3.64e-06 ***
## dur_distractor 0.15
## chosenItemdistractor < 2e-16 ***
## dur_target:dur_distractor < 2e-16 ***
## dur_target:chosenItemdistractor < 2e-16 ***
## dur_distractor:chosenItemdistractor 6.42e-12 ***
## dur_target:dur_distractor:chosenItemdistractor < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9604 on 55453 degrees of freedom
## Multiple R-squared: 0.06573, Adjusted R-squared: 0.06561
## F-statistic: 557.3 on 7 and 55453 DF, p-value: < 2.2e-16
Anova(f13)
## Anova Table (Type II tests)
##
## Response: conf_normalized
## Sum Sq Df F value Pr(>F)
## dur_target 99 1 107.4099 < 2.2e-16 ***
## dur_distractor 183 1 198.8686 < 2.2e-16 ***
## chosenItem 2723 1 2952.3154 < 2.2e-16 ***
## dur_target:dur_distractor 43 1 46.9304 7.432e-12 ***
## dur_target:chosenItem 49 1 53.5044 2.615e-13 ***
## dur_distractor:chosenItem 0 1 0.0072 0.9323
## dur_target:dur_distractor:chosenItem 71 1 77.2143 < 2.2e-16 ***
## Residuals 51151 55453
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f13, terms = c("dur_target", "dur_distractor", "chosenItem")))
f14 <- lm(conf_normalized ~ dur_target * factor(q_dur_distractor) * chosenItem,
data = subset(dat, dat$conf_normalized > -3 &
dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f14)
##
## Call:
## lm(formula = conf_normalized ~ dur_target * factor(q_dur_distractor) *
## chosenItem, data = subset(dat, dat$conf_normalized > -3 &
## dat$chosenItem != "dud" & dat$subj != "sub03"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9893 -0.6845 0.1217 0.6611 3.0949
##
## Coefficients:
## Estimate
## (Intercept) 0.2117361
## dur_target 0.1031362
## factor(q_dur_distractor)0.25 0.0006503
## factor(q_dur_distractor)0.5 -0.0572586
## factor(q_dur_distractor)0.75 -0.0050727
## chosenItemdistractor -0.5005380
## dur_target:factor(q_dur_distractor)0.25 -0.2477491
## dur_target:factor(q_dur_distractor)0.5 -0.2771062
## dur_target:factor(q_dur_distractor)0.75 -0.7915875
## dur_target:chosenItemdistractor -0.6119851
## factor(q_dur_distractor)0.25:chosenItemdistractor 0.0155866
## factor(q_dur_distractor)0.5:chosenItemdistractor 0.2452671
## factor(q_dur_distractor)0.75:chosenItemdistractor 0.0174391
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor 0.2219137
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor -0.3267649
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor 0.3912927
## Std. Error t value
## (Intercept) 0.0147487 14.356
## dur_target 0.0567846 1.816
## factor(q_dur_distractor)0.25 0.0225479 0.029
## factor(q_dur_distractor)0.5 0.0227952 -2.512
## factor(q_dur_distractor)0.75 0.0235686 -0.215
## chosenItemdistractor 0.0330045 -15.166
## dur_target:factor(q_dur_distractor)0.25 0.0792361 -3.127
## dur_target:factor(q_dur_distractor)0.5 0.0774608 -3.577
## dur_target:factor(q_dur_distractor)0.75 0.0757519 -10.450
## dur_target:chosenItemdistractor 0.1529327 -4.002
## factor(q_dur_distractor)0.25:chosenItemdistractor 0.0496037 0.314
## factor(q_dur_distractor)0.5:chosenItemdistractor 0.0452469 5.421
## factor(q_dur_distractor)0.75:chosenItemdistractor 0.0456319 0.382
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor 0.2070724 1.072
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor 0.1865373 -1.752
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor 0.1779828 2.198
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## dur_target 0.069334 .
## factor(q_dur_distractor)0.25 0.976991
## factor(q_dur_distractor)0.5 0.012012 *
## factor(q_dur_distractor)0.75 0.829587
## chosenItemdistractor < 2e-16 ***
## dur_target:factor(q_dur_distractor)0.25 0.001769 **
## dur_target:factor(q_dur_distractor)0.5 0.000347 ***
## dur_target:factor(q_dur_distractor)0.75 < 2e-16 ***
## dur_target:chosenItemdistractor 6.30e-05 ***
## factor(q_dur_distractor)0.25:chosenItemdistractor 0.753353
## factor(q_dur_distractor)0.5:chosenItemdistractor 5.96e-08 ***
## factor(q_dur_distractor)0.75:chosenItemdistractor 0.702338
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor 0.283872
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor 0.079824 .
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor 0.027919 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9592 on 55445 degrees of freedom
## Multiple R-squared: 0.06831, Adjusted R-squared: 0.06806
## F-statistic: 271 on 15 and 55445 DF, p-value: < 2.2e-16
Anova(f14)
## Anova Table (Type II tests)
##
## Response: conf_normalized
## Sum Sq Df F value Pr(>F)
## dur_target 248 1 269.8508 < 2.2e-16
## factor(q_dur_distractor) 262 3 94.8801 < 2.2e-16
## chosenItem 2647 1 2876.8279 < 2.2e-16
## dur_target:factor(q_dur_distractor) 105 3 37.9460 < 2.2e-16
## dur_target:chosenItem 73 1 79.5544 < 2.2e-16
## factor(q_dur_distractor):chosenItem 31 3 11.2748 2.168e-07
## dur_target:factor(q_dur_distractor):chosenItem 25 3 9.1304 4.889e-06
## Residuals 51010 55445
##
## dur_target ***
## factor(q_dur_distractor) ***
## chosenItem ***
## dur_target:factor(q_dur_distractor) ***
## dur_target:chosenItem ***
## factor(q_dur_distractor):chosenItem ***
## dur_target:factor(q_dur_distractor):chosenItem ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f14, terms = c("dur_target", "q_dur_distractor", "chosenItem")))
# condition separated
ggplot(subset(dat, dat$conf_normalized > -3 & dat$chosenItem != "dud"), aes(x = dur_target, y = conf_normalized, color = factor(q_dur_distractor))) +
geom_count(alpha = 0.5) + stat_smooth(size = 1.2) +
scale_x_continuous(breaks = seq(0, 0.6, 0.2), limits = c(0, 0.6)) + ylim(-3, 3) + facet_nested(. ~ chosenItem + condition)
## Warning: Removed 2595 rows containing non-finite values (`stat_sum()`).
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
## Warning: Removed 2595 rows containing non-finite values (`stat_smooth()`).
f15 <- lm(conf_normalized ~ dur_target * dur_distractor * chosenItem * factor(condition),
data = subset(dat, dat$conf_normalized > -3 &
dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f15)
##
## Call:
## lm(formula = conf_normalized ~ dur_target * dur_distractor *
## chosenItem * factor(condition), data = subset(dat, dat$conf_normalized >
## -3 & dat$chosenItem != "dud" & dat$subj != "sub03"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1480 -0.6892 0.1239 0.7156 3.2792
##
## Coefficients:
## Estimate
## (Intercept) 0.152589
## dur_target 0.393589
## dur_distractor 0.032907
## chosenItemdistractor -0.391511
## factor(condition)0.3 0.265257
## factor(condition)0.5 0.213857
## factor(condition)0.7 0.020696
## factor(condition)0.85 -0.101493
## factor(condition)0.95 -0.247144
## dur_target:dur_distractor -1.729456
## dur_target:chosenItemdistractor -1.615441
## dur_distractor:chosenItemdistractor -0.108707
## dur_target:factor(condition)0.3 -0.544545
## dur_target:factor(condition)0.5 -0.541084
## dur_target:factor(condition)0.7 0.119335
## dur_target:factor(condition)0.85 0.028688
## dur_target:factor(condition)0.95 0.110702
## dur_distractor:factor(condition)0.3 0.015035
## dur_distractor:factor(condition)0.5 0.100398
## dur_distractor:factor(condition)0.7 0.303708
## dur_distractor:factor(condition)0.85 -0.088502
## dur_distractor:factor(condition)0.95 -0.681138
## chosenItemdistractor:factor(condition)0.3 0.141430
## chosenItemdistractor:factor(condition)0.5 0.154115
## chosenItemdistractor:factor(condition)0.7 0.244318
## chosenItemdistractor:factor(condition)0.85 -0.012806
## chosenItemdistractor:factor(condition)0.95 0.017849
## dur_target:dur_distractor:chosenItemdistractor 1.716865
## dur_target:dur_distractor:factor(condition)0.3 0.762466
## dur_target:dur_distractor:factor(condition)0.5 0.687460
## dur_target:dur_distractor:factor(condition)0.7 -1.065963
## dur_target:dur_distractor:factor(condition)0.85 0.177283
## dur_target:dur_distractor:factor(condition)0.95 0.729956
## dur_target:chosenItemdistractor:factor(condition)0.3 0.687209
## dur_target:chosenItemdistractor:factor(condition)0.5 0.185473
## dur_target:chosenItemdistractor:factor(condition)0.7 0.426613
## dur_target:chosenItemdistractor:factor(condition)0.85 0.234246
## dur_target:chosenItemdistractor:factor(condition)0.95 0.832629
## dur_distractor:chosenItemdistractor:factor(condition)0.3 -0.587589
## dur_distractor:chosenItemdistractor:factor(condition)0.5 -1.224366
## dur_distractor:chosenItemdistractor:factor(condition)0.7 -1.126309
## dur_distractor:chosenItemdistractor:factor(condition)0.85 -0.032058
## dur_distractor:chosenItemdistractor:factor(condition)0.95 0.397322
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.3 0.003624
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.5 2.507014
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.7 2.090784
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.85 0.655318
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.95 -1.439353
## Std. Error
## (Intercept) 0.034208
## dur_target 0.117228
## dur_distractor 0.120425
## chosenItemdistractor 0.065989
## factor(condition)0.3 0.046789
## factor(condition)0.5 0.046042
## factor(condition)0.7 0.045555
## factor(condition)0.85 0.045193
## factor(condition)0.95 0.045431
## dur_target:dur_distractor 0.301644
## dur_target:chosenItemdistractor 0.241943
## dur_distractor:chosenItemdistractor 0.243158
## dur_target:factor(condition)0.3 0.169995
## dur_target:factor(condition)0.5 0.164764
## dur_target:factor(condition)0.7 0.167722
## dur_target:factor(condition)0.85 0.162087
## dur_target:factor(condition)0.95 0.162991
## dur_distractor:factor(condition)0.3 0.170765
## dur_distractor:factor(condition)0.5 0.174232
## dur_distractor:factor(condition)0.7 0.179765
## dur_distractor:factor(condition)0.85 0.180007
## dur_distractor:factor(condition)0.95 0.182201
## chosenItemdistractor:factor(condition)0.3 0.088741
## chosenItemdistractor:factor(condition)0.5 0.089845
## chosenItemdistractor:factor(condition)0.7 0.090032
## chosenItemdistractor:factor(condition)0.85 0.088377
## chosenItemdistractor:factor(condition)0.95 0.092938
## dur_target:dur_distractor:chosenItemdistractor 0.587224
## dur_target:dur_distractor:factor(condition)0.3 0.447612
## dur_target:dur_distractor:factor(condition)0.5 0.447423
## dur_target:dur_distractor:factor(condition)0.7 0.473620
## dur_target:dur_distractor:factor(condition)0.85 0.478851
## dur_target:dur_distractor:factor(condition)0.95 0.485916
## dur_target:chosenItemdistractor:factor(condition)0.3 0.335010
## dur_target:chosenItemdistractor:factor(condition)0.5 0.350785
## dur_target:chosenItemdistractor:factor(condition)0.7 0.375173
## dur_target:chosenItemdistractor:factor(condition)0.85 0.352899
## dur_target:chosenItemdistractor:factor(condition)0.95 0.368551
## dur_distractor:chosenItemdistractor:factor(condition)0.3 0.335203
## dur_distractor:chosenItemdistractor:factor(condition)0.5 0.355135
## dur_distractor:chosenItemdistractor:factor(condition)0.7 0.365782
## dur_distractor:chosenItemdistractor:factor(condition)0.85 0.347526
## dur_distractor:chosenItemdistractor:factor(condition)0.95 0.365578
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.3 0.790824
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.5 0.897349
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.7 1.015129
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.85 0.931406
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.95 1.007507
## t value
## (Intercept) 4.461
## dur_target 3.357
## dur_distractor 0.273
## chosenItemdistractor -5.933
## factor(condition)0.3 5.669
## factor(condition)0.5 4.645
## factor(condition)0.7 0.454
## factor(condition)0.85 -2.246
## factor(condition)0.95 -5.440
## dur_target:dur_distractor -5.733
## dur_target:chosenItemdistractor -6.677
## dur_distractor:chosenItemdistractor -0.447
## dur_target:factor(condition)0.3 -3.203
## dur_target:factor(condition)0.5 -3.284
## dur_target:factor(condition)0.7 0.712
## dur_target:factor(condition)0.85 0.177
## dur_target:factor(condition)0.95 0.679
## dur_distractor:factor(condition)0.3 0.088
## dur_distractor:factor(condition)0.5 0.576
## dur_distractor:factor(condition)0.7 1.689
## dur_distractor:factor(condition)0.85 -0.492
## dur_distractor:factor(condition)0.95 -3.738
## chosenItemdistractor:factor(condition)0.3 1.594
## chosenItemdistractor:factor(condition)0.5 1.715
## chosenItemdistractor:factor(condition)0.7 2.714
## chosenItemdistractor:factor(condition)0.85 -0.145
## chosenItemdistractor:factor(condition)0.95 0.192
## dur_target:dur_distractor:chosenItemdistractor 2.924
## dur_target:dur_distractor:factor(condition)0.3 1.703
## dur_target:dur_distractor:factor(condition)0.5 1.536
## dur_target:dur_distractor:factor(condition)0.7 -2.251
## dur_target:dur_distractor:factor(condition)0.85 0.370
## dur_target:dur_distractor:factor(condition)0.95 1.502
## dur_target:chosenItemdistractor:factor(condition)0.3 2.051
## dur_target:chosenItemdistractor:factor(condition)0.5 0.529
## dur_target:chosenItemdistractor:factor(condition)0.7 1.137
## dur_target:chosenItemdistractor:factor(condition)0.85 0.664
## dur_target:chosenItemdistractor:factor(condition)0.95 2.259
## dur_distractor:chosenItemdistractor:factor(condition)0.3 -1.753
## dur_distractor:chosenItemdistractor:factor(condition)0.5 -3.448
## dur_distractor:chosenItemdistractor:factor(condition)0.7 -3.079
## dur_distractor:chosenItemdistractor:factor(condition)0.85 -0.092
## dur_distractor:chosenItemdistractor:factor(condition)0.95 1.087
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.3 0.005
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.5 2.794
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.7 2.060
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.85 0.704
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.95 -1.429
## Pr(>|t|)
## (Intercept) 8.19e-06
## dur_target 0.000787
## dur_distractor 0.784660
## chosenItemdistractor 2.99e-09
## factor(condition)0.3 1.44e-08
## factor(condition)0.5 3.41e-06
## factor(condition)0.7 0.649610
## factor(condition)0.85 0.024723
## factor(condition)0.95 5.35e-08
## dur_target:dur_distractor 9.89e-09
## dur_target:chosenItemdistractor 2.46e-11
## dur_distractor:chosenItemdistractor 0.654830
## dur_target:factor(condition)0.3 0.001359
## dur_target:factor(condition)0.5 0.001024
## dur_target:factor(condition)0.7 0.476774
## dur_target:factor(condition)0.85 0.859514
## dur_target:factor(condition)0.95 0.497022
## dur_distractor:factor(condition)0.3 0.929839
## dur_distractor:factor(condition)0.5 0.564460
## dur_distractor:factor(condition)0.7 0.091135
## dur_distractor:factor(condition)0.85 0.622961
## dur_distractor:factor(condition)0.95 0.000185
## chosenItemdistractor:factor(condition)0.3 0.111002
## chosenItemdistractor:factor(condition)0.5 0.086289
## chosenItemdistractor:factor(condition)0.7 0.006656
## chosenItemdistractor:factor(condition)0.85 0.884790
## chosenItemdistractor:factor(condition)0.95 0.847702
## dur_target:dur_distractor:chosenItemdistractor 0.003460
## dur_target:dur_distractor:factor(condition)0.3 0.088498
## dur_target:dur_distractor:factor(condition)0.5 0.124424
## dur_target:dur_distractor:factor(condition)0.7 0.024410
## dur_target:dur_distractor:factor(condition)0.85 0.711215
## dur_target:dur_distractor:factor(condition)0.95 0.133044
## dur_target:chosenItemdistractor:factor(condition)0.3 0.040242
## dur_target:chosenItemdistractor:factor(condition)0.5 0.596990
## dur_target:chosenItemdistractor:factor(condition)0.7 0.255496
## dur_target:chosenItemdistractor:factor(condition)0.85 0.506837
## dur_target:chosenItemdistractor:factor(condition)0.95 0.023875
## dur_distractor:chosenItemdistractor:factor(condition)0.3 0.079619
## dur_distractor:chosenItemdistractor:factor(condition)0.5 0.000566
## dur_distractor:chosenItemdistractor:factor(condition)0.7 0.002077
## dur_distractor:chosenItemdistractor:factor(condition)0.85 0.926502
## dur_distractor:chosenItemdistractor:factor(condition)0.95 0.277115
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.3 0.996344
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.5 0.005211
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.7 0.039439
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.85 0.481697
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.95 0.153117
##
## (Intercept) ***
## dur_target ***
## dur_distractor
## chosenItemdistractor ***
## factor(condition)0.3 ***
## factor(condition)0.5 ***
## factor(condition)0.7
## factor(condition)0.85 *
## factor(condition)0.95 ***
## dur_target:dur_distractor ***
## dur_target:chosenItemdistractor ***
## dur_distractor:chosenItemdistractor
## dur_target:factor(condition)0.3 **
## dur_target:factor(condition)0.5 **
## dur_target:factor(condition)0.7
## dur_target:factor(condition)0.85
## dur_target:factor(condition)0.95
## dur_distractor:factor(condition)0.3
## dur_distractor:factor(condition)0.5
## dur_distractor:factor(condition)0.7 .
## dur_distractor:factor(condition)0.85
## dur_distractor:factor(condition)0.95 ***
## chosenItemdistractor:factor(condition)0.3
## chosenItemdistractor:factor(condition)0.5 .
## chosenItemdistractor:factor(condition)0.7 **
## chosenItemdistractor:factor(condition)0.85
## chosenItemdistractor:factor(condition)0.95
## dur_target:dur_distractor:chosenItemdistractor **
## dur_target:dur_distractor:factor(condition)0.3 .
## dur_target:dur_distractor:factor(condition)0.5
## dur_target:dur_distractor:factor(condition)0.7 *
## dur_target:dur_distractor:factor(condition)0.85
## dur_target:dur_distractor:factor(condition)0.95
## dur_target:chosenItemdistractor:factor(condition)0.3 *
## dur_target:chosenItemdistractor:factor(condition)0.5
## dur_target:chosenItemdistractor:factor(condition)0.7
## dur_target:chosenItemdistractor:factor(condition)0.85
## dur_target:chosenItemdistractor:factor(condition)0.95 *
## dur_distractor:chosenItemdistractor:factor(condition)0.3 .
## dur_distractor:chosenItemdistractor:factor(condition)0.5 ***
## dur_distractor:chosenItemdistractor:factor(condition)0.7 **
## dur_distractor:chosenItemdistractor:factor(condition)0.85
## dur_distractor:chosenItemdistractor:factor(condition)0.95
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.3
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.5 **
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.7 *
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.85
## dur_target:dur_distractor:chosenItemdistractor:factor(condition)0.95
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9449 on 55413 degrees of freedom
## Multiple R-squared: 0.09629, Adjusted R-squared: 0.09553
## F-statistic: 125.6 on 47 and 55413 DF, p-value: < 2.2e-16
Anova(f15)
## Anova Table (Type II tests)
##
## Response: conf_normalized
## Sum Sq Df F value
## dur_target 89 1 99.2917
## dur_distractor 250 1 280.3116
## chosenItem 2680 1 3001.2125
## factor(condition) 1471 5 329.4529
## dur_target:dur_distractor 47 1 52.3358
## dur_target:chosenItem 62 1 69.1842
## dur_distractor:chosenItem 6 1 6.5299
## dur_target:factor(condition) 48 5 10.7546
## dur_distractor:factor(condition) 55 5 12.3967
## chosenItem:factor(condition) 56 5 12.5547
## dur_target:dur_distractor:chosenItem 63 1 70.2299
## dur_target:dur_distractor:factor(condition) 21 5 4.6015
## dur_target:chosenItem:factor(condition) 25 5 5.5651
## dur_distractor:chosenItem:factor(condition) 20 5 4.5739
## dur_target:dur_distractor:chosenItem:factor(condition) 18 5 3.9291
## Residuals 49478 55413
## Pr(>F)
## dur_target < 2.2e-16 ***
## dur_distractor < 2.2e-16 ***
## chosenItem < 2.2e-16 ***
## factor(condition) < 2.2e-16 ***
## dur_target:dur_distractor 4.738e-13 ***
## dur_target:chosenItem < 2.2e-16 ***
## dur_distractor:chosenItem 0.0106102 *
## dur_target:factor(condition) 2.361e-10 ***
## dur_distractor:factor(condition) 4.801e-12 ***
## chosenItem:factor(condition) 3.296e-12 ***
## dur_target:dur_distractor:chosenItem < 2.2e-16 ***
## dur_target:dur_distractor:factor(condition) 0.0003370 ***
## dur_target:chosenItem:factor(condition) 3.948e-05 ***
## dur_distractor:chosenItem:factor(condition) 0.0003582 ***
## dur_target:dur_distractor:chosenItem:factor(condition) 0.0014582 **
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f15, terms = c("dur_target", "dur_distractor", "chosenItem", "condition")))
f16 <- lm(conf_normalized ~ dur_target * factor(q_dur_distractor) * chosenItem * factor(condition),
data = subset(dat, dat$conf_normalized > -3 &
dat$chosenItem != "dud" & dat$subj != "sub03"))
summary(f16)
##
## Call:
## lm(formula = conf_normalized ~ dur_target * factor(q_dur_distractor) *
## chosenItem * factor(condition), data = subset(dat, dat$conf_normalized >
## -3 & dat$chosenItem != "dud" & dat$subj != "sub03"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1915 -0.6923 0.1254 0.7321 3.1605
##
## Coefficients:
## Estimate
## (Intercept) 0.223556
## dur_target 0.231218
## factor(q_dur_distractor)0.25 0.017234
## factor(q_dur_distractor)0.5 -0.033788
## factor(q_dur_distractor)0.75 -0.064629
## chosenItemdistractor -0.592958
## factor(condition)0.3 0.215666
## factor(condition)0.5 0.145828
## factor(condition)0.7 0.085599
## factor(condition)0.85 -0.093410
## factor(condition)0.95 -0.359643
## dur_target:factor(q_dur_distractor)0.25 -0.432461
## dur_target:factor(q_dur_distractor)0.5 -0.347522
## dur_target:factor(q_dur_distractor)0.75 -0.915591
## dur_target:chosenItemdistractor -0.910884
## factor(q_dur_distractor)0.25:chosenItemdistractor 0.037269
## factor(q_dur_distractor)0.5:chosenItemdistractor 0.214572
## factor(q_dur_distractor)0.75:chosenItemdistractor 0.218474
## dur_target:factor(condition)0.3 -0.287335
## dur_target:factor(condition)0.5 -0.099180
## dur_target:factor(condition)0.7 -0.232420
## dur_target:factor(condition)0.85 -0.050718
## dur_target:factor(condition)0.95 0.241256
## factor(q_dur_distractor)0.25:factor(condition)0.3 -0.070835
## factor(q_dur_distractor)0.5:factor(condition)0.3 0.081544
## factor(q_dur_distractor)0.75:factor(condition)0.3 0.038657
## factor(q_dur_distractor)0.25:factor(condition)0.5 0.045339
## factor(q_dur_distractor)0.5:factor(condition)0.5 0.083681
## factor(q_dur_distractor)0.75:factor(condition)0.5 0.047860
## factor(q_dur_distractor)0.25:factor(condition)0.7 -0.135422
## factor(q_dur_distractor)0.5:factor(condition)0.7 -0.098963
## factor(q_dur_distractor)0.75:factor(condition)0.7 0.159849
## factor(q_dur_distractor)0.25:factor(condition)0.85 -0.098060
## factor(q_dur_distractor)0.5:factor(condition)0.85 -0.163805
## factor(q_dur_distractor)0.75:factor(condition)0.85 0.053643
## factor(q_dur_distractor)0.25:factor(condition)0.95 0.118454
## factor(q_dur_distractor)0.5:factor(condition)0.95 -0.077289
## factor(q_dur_distractor)0.75:factor(condition)0.95 -0.101110
## chosenItemdistractor:factor(condition)0.3 0.211186
## chosenItemdistractor:factor(condition)0.5 0.040706
## chosenItemdistractor:factor(condition)0.7 0.251905
## chosenItemdistractor:factor(condition)0.85 -0.122312
## chosenItemdistractor:factor(condition)0.95 0.130622
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor 0.290576
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor -0.654354
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor 0.264103
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3 0.165689
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3 -0.046161
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3 0.216786
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5 -0.216620
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5 -0.329261
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5 0.152905
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7 0.619141
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7 0.101614
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7 -0.345307
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85 0.318844
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85 0.377570
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85 -0.124471
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95 -0.129717
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95 -0.491945
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95 0.227690
## dur_target:chosenItemdistractor:factor(condition)0.3 -0.084827
## dur_target:chosenItemdistractor:factor(condition)0.5 0.055463
## dur_target:chosenItemdistractor:factor(condition)0.7 0.169597
## dur_target:chosenItemdistractor:factor(condition)0.85 0.833248
## dur_target:chosenItemdistractor:factor(condition)0.95 0.448712
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3 0.035184
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3 -0.203738
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3 -0.306369
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5 0.127348
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5 -0.067986
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5 -0.276096
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7 -0.073422
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7 -0.158742
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7 -0.409466
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85 -0.043762
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85 0.473127
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85 -0.001868
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95 -0.253357
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95 0.154311
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95 -0.034956
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3 -0.034721
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3 1.472245
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3 0.457927
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5 -0.356454
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5 1.183369
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5 0.726738
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7 -0.221615
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7 1.007585
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7 0.898216
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85 0.217956
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85 -1.409832
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85 -0.081669
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95 0.973096
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95 0.475799
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95 -0.736355
## Std. Error
## (Intercept) 0.046291
## dur_target 0.179126
## factor(q_dur_distractor)0.25 0.064666
## factor(q_dur_distractor)0.5 0.059877
## factor(q_dur_distractor)0.75 0.060126
## chosenItemdistractor 0.099328
## factor(condition)0.3 0.060811
## factor(condition)0.5 0.058990
## factor(condition)0.7 0.057062
## factor(condition)0.85 0.056326
## factor(condition)0.95 0.056008
## dur_target:factor(q_dur_distractor)0.25 0.222154
## dur_target:factor(q_dur_distractor)0.5 0.207899
## dur_target:factor(q_dur_distractor)0.75 0.205040
## dur_target:chosenItemdistractor 0.441662
## factor(q_dur_distractor)0.25:chosenItemdistractor 0.138975
## factor(q_dur_distractor)0.5:chosenItemdistractor 0.124154
## factor(q_dur_distractor)0.75:chosenItemdistractor 0.123206
## dur_target:factor(condition)0.3 0.245744
## dur_target:factor(condition)0.5 0.231151
## dur_target:factor(condition)0.7 0.224252
## dur_target:factor(condition)0.85 0.213967
## dur_target:factor(condition)0.95 0.212110
## factor(q_dur_distractor)0.25:factor(condition)0.3 0.086654
## factor(q_dur_distractor)0.5:factor(condition)0.3 0.084045
## factor(q_dur_distractor)0.75:factor(condition)0.3 0.082460
## factor(q_dur_distractor)0.25:factor(condition)0.5 0.084612
## factor(q_dur_distractor)0.5:factor(condition)0.5 0.081842
## factor(q_dur_distractor)0.75:factor(condition)0.5 0.083043
## factor(q_dur_distractor)0.25:factor(condition)0.7 0.082723
## factor(q_dur_distractor)0.5:factor(condition)0.7 0.079608
## factor(q_dur_distractor)0.75:factor(condition)0.7 0.084492
## factor(q_dur_distractor)0.25:factor(condition)0.85 0.082234
## factor(q_dur_distractor)0.5:factor(condition)0.85 0.081642
## factor(q_dur_distractor)0.75:factor(condition)0.85 0.083937
## factor(q_dur_distractor)0.25:factor(condition)0.95 0.082921
## factor(q_dur_distractor)0.5:factor(condition)0.95 0.080497
## factor(q_dur_distractor)0.75:factor(condition)0.95 0.083808
## chosenItemdistractor:factor(condition)0.3 0.128129
## chosenItemdistractor:factor(condition)0.5 0.126091
## chosenItemdistractor:factor(condition)0.7 0.126703
## chosenItemdistractor:factor(condition)0.85 0.121746
## chosenItemdistractor:factor(condition)0.95 0.127465
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor 0.548733
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor 0.503543
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor 0.483339
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3 0.309156
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3 0.299989
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3 0.286842
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5 0.298857
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5 0.283407
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5 0.278439
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7 0.292302
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7 0.275636
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7 0.281156
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85 0.282496
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85 0.280028
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85 0.277505
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95 0.284016
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95 0.277561
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95 0.270381
## dur_target:chosenItemdistractor:factor(condition)0.3 0.568313
## dur_target:chosenItemdistractor:factor(condition)0.5 0.581960
## dur_target:chosenItemdistractor:factor(condition)0.7 0.608711
## dur_target:chosenItemdistractor:factor(condition)0.85 0.537326
## dur_target:chosenItemdistractor:factor(condition)0.95 0.582734
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3 0.182374
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3 0.170034
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3 0.164694
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5 0.182623
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5 0.162618
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5 0.164189
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7 0.180433
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7 0.163839
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7 0.165458
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85 0.180329
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85 0.161587
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85 0.162678
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95 0.184953
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95 0.165877
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95 0.167553
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3 0.721095
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3 0.685843
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3 0.632029
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5 0.743645
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5 0.668697
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5 0.652803
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7 0.781753
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7 0.709184
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7 0.683981
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85 0.731626
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85 0.654292
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85 0.622147
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95 0.761565
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95 0.684138
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95 0.664687
## t value
## (Intercept) 4.829
## dur_target 1.291
## factor(q_dur_distractor)0.25 0.267
## factor(q_dur_distractor)0.5 -0.564
## factor(q_dur_distractor)0.75 -1.075
## chosenItemdistractor -5.970
## factor(condition)0.3 3.547
## factor(condition)0.5 2.472
## factor(condition)0.7 1.500
## factor(condition)0.85 -1.658
## factor(condition)0.95 -6.421
## dur_target:factor(q_dur_distractor)0.25 -1.947
## dur_target:factor(q_dur_distractor)0.5 -1.672
## dur_target:factor(q_dur_distractor)0.75 -4.465
## dur_target:chosenItemdistractor -2.062
## factor(q_dur_distractor)0.25:chosenItemdistractor 0.268
## factor(q_dur_distractor)0.5:chosenItemdistractor 1.728
## factor(q_dur_distractor)0.75:chosenItemdistractor 1.773
## dur_target:factor(condition)0.3 -1.169
## dur_target:factor(condition)0.5 -0.429
## dur_target:factor(condition)0.7 -1.036
## dur_target:factor(condition)0.85 -0.237
## dur_target:factor(condition)0.95 1.137
## factor(q_dur_distractor)0.25:factor(condition)0.3 -0.817
## factor(q_dur_distractor)0.5:factor(condition)0.3 0.970
## factor(q_dur_distractor)0.75:factor(condition)0.3 0.469
## factor(q_dur_distractor)0.25:factor(condition)0.5 0.536
## factor(q_dur_distractor)0.5:factor(condition)0.5 1.022
## factor(q_dur_distractor)0.75:factor(condition)0.5 0.576
## factor(q_dur_distractor)0.25:factor(condition)0.7 -1.637
## factor(q_dur_distractor)0.5:factor(condition)0.7 -1.243
## factor(q_dur_distractor)0.75:factor(condition)0.7 1.892
## factor(q_dur_distractor)0.25:factor(condition)0.85 -1.192
## factor(q_dur_distractor)0.5:factor(condition)0.85 -2.006
## factor(q_dur_distractor)0.75:factor(condition)0.85 0.639
## factor(q_dur_distractor)0.25:factor(condition)0.95 1.429
## factor(q_dur_distractor)0.5:factor(condition)0.95 -0.960
## factor(q_dur_distractor)0.75:factor(condition)0.95 -1.206
## chosenItemdistractor:factor(condition)0.3 1.648
## chosenItemdistractor:factor(condition)0.5 0.323
## chosenItemdistractor:factor(condition)0.7 1.988
## chosenItemdistractor:factor(condition)0.85 -1.005
## chosenItemdistractor:factor(condition)0.95 1.025
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor 0.530
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor -1.300
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor 0.546
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3 0.536
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3 -0.154
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3 0.756
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5 -0.725
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5 -1.162
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5 0.549
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7 2.118
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7 0.369
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7 -1.228
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85 1.129
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85 1.348
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85 -0.449
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95 -0.457
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95 -1.772
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95 0.842
## dur_target:chosenItemdistractor:factor(condition)0.3 -0.149
## dur_target:chosenItemdistractor:factor(condition)0.5 0.095
## dur_target:chosenItemdistractor:factor(condition)0.7 0.279
## dur_target:chosenItemdistractor:factor(condition)0.85 1.551
## dur_target:chosenItemdistractor:factor(condition)0.95 0.770
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3 0.193
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3 -1.198
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3 -1.860
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5 0.697
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5 -0.418
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5 -1.682
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7 -0.407
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7 -0.969
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7 -2.475
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85 -0.243
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85 2.928
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85 -0.011
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95 -1.370
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95 0.930
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95 -0.209
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3 -0.048
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3 2.147
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3 0.725
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5 -0.479
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5 1.770
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5 1.113
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7 -0.283
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7 1.421
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7 1.313
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85 0.298
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85 -2.155
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85 -0.131
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95 1.278
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95 0.695
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95 -1.108
## Pr(>|t|)
## (Intercept) 1.37e-06
## dur_target 0.196775
## factor(q_dur_distractor)0.25 0.789849
## factor(q_dur_distractor)0.5 0.572551
## factor(q_dur_distractor)0.75 0.282427
## chosenItemdistractor 2.39e-09
## factor(condition)0.3 0.000391
## factor(condition)0.5 0.013436
## factor(condition)0.7 0.133596
## factor(condition)0.85 0.097246
## factor(condition)0.95 1.36e-10
## dur_target:factor(q_dur_distractor)0.25 0.051580
## dur_target:factor(q_dur_distractor)0.5 0.094610
## dur_target:factor(q_dur_distractor)0.75 8.01e-06
## dur_target:chosenItemdistractor 0.039174
## factor(q_dur_distractor)0.25:chosenItemdistractor 0.788568
## factor(q_dur_distractor)0.5:chosenItemdistractor 0.083946
## factor(q_dur_distractor)0.75:chosenItemdistractor 0.076194
## dur_target:factor(condition)0.3 0.242309
## dur_target:factor(condition)0.5 0.667874
## dur_target:factor(condition)0.7 0.300009
## dur_target:factor(condition)0.85 0.812629
## dur_target:factor(condition)0.95 0.255372
## factor(q_dur_distractor)0.25:factor(condition)0.3 0.413680
## factor(q_dur_distractor)0.5:factor(condition)0.3 0.331929
## factor(q_dur_distractor)0.75:factor(condition)0.3 0.639221
## factor(q_dur_distractor)0.25:factor(condition)0.5 0.592068
## factor(q_dur_distractor)0.5:factor(condition)0.5 0.306563
## factor(q_dur_distractor)0.75:factor(condition)0.5 0.564394
## factor(q_dur_distractor)0.25:factor(condition)0.7 0.101624
## factor(q_dur_distractor)0.5:factor(condition)0.7 0.213826
## factor(q_dur_distractor)0.75:factor(condition)0.7 0.058513
## factor(q_dur_distractor)0.25:factor(condition)0.85 0.233089
## factor(q_dur_distractor)0.5:factor(condition)0.85 0.044821
## factor(q_dur_distractor)0.75:factor(condition)0.85 0.522764
## factor(q_dur_distractor)0.25:factor(condition)0.95 0.153148
## factor(q_dur_distractor)0.5:factor(condition)0.95 0.336988
## factor(q_dur_distractor)0.75:factor(condition)0.95 0.227648
## chosenItemdistractor:factor(condition)0.3 0.099312
## chosenItemdistractor:factor(condition)0.5 0.746822
## chosenItemdistractor:factor(condition)0.7 0.046800
## chosenItemdistractor:factor(condition)0.85 0.315070
## chosenItemdistractor:factor(condition)0.95 0.305477
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor 0.596433
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor 0.193778
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor 0.584783
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3 0.592002
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3 0.877709
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3 0.449790
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5 0.468560
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5 0.245324
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5 0.582904
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7 0.034166
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7 0.712389
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7 0.219390
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85 0.259044
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85 0.177559
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85 0.653767
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95 0.647871
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95 0.076336
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95 0.399730
## dur_target:chosenItemdistractor:factor(condition)0.3 0.881348
## dur_target:chosenItemdistractor:factor(condition)0.5 0.924073
## dur_target:chosenItemdistractor:factor(condition)0.7 0.780540
## dur_target:chosenItemdistractor:factor(condition)0.85 0.120972
## dur_target:chosenItemdistractor:factor(condition)0.95 0.441296
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3 0.847019
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3 0.230835
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3 0.062858
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5 0.485602
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5 0.675895
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5 0.092657
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7 0.684068
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7 0.332605
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7 0.013336
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85 0.808256
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85 0.003413
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85 0.990839
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95 0.170740
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95 0.352232
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95 0.834739
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3 0.961597
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3 0.031828
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3 0.468741
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5 0.631703
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5 0.076789
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5 0.265603
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7 0.776806
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7 0.155390
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7 0.189115
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85 0.765776
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85 0.031186
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85 0.895563
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95 0.201340
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95 0.486762
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95 0.267944
##
## (Intercept) ***
## dur_target
## factor(q_dur_distractor)0.25
## factor(q_dur_distractor)0.5
## factor(q_dur_distractor)0.75
## chosenItemdistractor ***
## factor(condition)0.3 ***
## factor(condition)0.5 *
## factor(condition)0.7
## factor(condition)0.85 .
## factor(condition)0.95 ***
## dur_target:factor(q_dur_distractor)0.25 .
## dur_target:factor(q_dur_distractor)0.5 .
## dur_target:factor(q_dur_distractor)0.75 ***
## dur_target:chosenItemdistractor *
## factor(q_dur_distractor)0.25:chosenItemdistractor
## factor(q_dur_distractor)0.5:chosenItemdistractor .
## factor(q_dur_distractor)0.75:chosenItemdistractor .
## dur_target:factor(condition)0.3
## dur_target:factor(condition)0.5
## dur_target:factor(condition)0.7
## dur_target:factor(condition)0.85
## dur_target:factor(condition)0.95
## factor(q_dur_distractor)0.25:factor(condition)0.3
## factor(q_dur_distractor)0.5:factor(condition)0.3
## factor(q_dur_distractor)0.75:factor(condition)0.3
## factor(q_dur_distractor)0.25:factor(condition)0.5
## factor(q_dur_distractor)0.5:factor(condition)0.5
## factor(q_dur_distractor)0.75:factor(condition)0.5
## factor(q_dur_distractor)0.25:factor(condition)0.7
## factor(q_dur_distractor)0.5:factor(condition)0.7
## factor(q_dur_distractor)0.75:factor(condition)0.7 .
## factor(q_dur_distractor)0.25:factor(condition)0.85
## factor(q_dur_distractor)0.5:factor(condition)0.85 *
## factor(q_dur_distractor)0.75:factor(condition)0.85
## factor(q_dur_distractor)0.25:factor(condition)0.95
## factor(q_dur_distractor)0.5:factor(condition)0.95
## factor(q_dur_distractor)0.75:factor(condition)0.95
## chosenItemdistractor:factor(condition)0.3 .
## chosenItemdistractor:factor(condition)0.5
## chosenItemdistractor:factor(condition)0.7 *
## chosenItemdistractor:factor(condition)0.85
## chosenItemdistractor:factor(condition)0.95
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.3
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.3
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.3
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.5
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.5
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.5
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.7 *
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.7
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.7
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.85
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.85
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.85
## dur_target:factor(q_dur_distractor)0.25:factor(condition)0.95
## dur_target:factor(q_dur_distractor)0.5:factor(condition)0.95 .
## dur_target:factor(q_dur_distractor)0.75:factor(condition)0.95
## dur_target:chosenItemdistractor:factor(condition)0.3
## dur_target:chosenItemdistractor:factor(condition)0.5
## dur_target:chosenItemdistractor:factor(condition)0.7
## dur_target:chosenItemdistractor:factor(condition)0.85
## dur_target:chosenItemdistractor:factor(condition)0.95
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3 .
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5 .
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7 *
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85 **
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85
## factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95
## factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95
## factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.3
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.3 *
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.3
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.5
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.5 .
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.5
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.7
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.7
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.7
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.85
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.85 *
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.85
## dur_target:factor(q_dur_distractor)0.25:chosenItemdistractor:factor(condition)0.95
## dur_target:factor(q_dur_distractor)0.5:chosenItemdistractor:factor(condition)0.95
## dur_target:factor(q_dur_distractor)0.75:chosenItemdistractor:factor(condition)0.95
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9421 on 55365 degrees of freedom
## Multiple R-squared: 0.1025, Adjusted R-squared: 0.1009
## F-statistic: 66.53 on 95 and 55365 DF, p-value: < 2.2e-16
Anova(f16)
## Anova Table (Type II tests)
##
## Response: conf_normalized
## Sum Sq Df
## dur_target 260 1
## factor(q_dur_distractor) 365 3
## chosenItem 2584 1
## factor(condition) 1542 5
## dur_target:factor(q_dur_distractor) 121 3
## dur_target:chosenItem 64 1
## factor(q_dur_distractor):chosenItem 44 3
## dur_target:factor(condition) 23 5
## factor(q_dur_distractor):factor(condition) 83 15
## chosenItem:factor(condition) 63 5
## dur_target:factor(q_dur_distractor):chosenItem 22 3
## dur_target:factor(q_dur_distractor):factor(condition) 44 15
## dur_target:chosenItem:factor(condition) 18 5
## factor(q_dur_distractor):chosenItem:factor(condition) 41 15
## dur_target:factor(q_dur_distractor):chosenItem:factor(condition) 65 15
## Residuals 49140 55365
## F value
## dur_target 292.8945
## factor(q_dur_distractor) 136.9238
## chosenItem 2911.8097
## factor(condition) 347.4894
## dur_target:factor(q_dur_distractor) 45.2985
## dur_target:chosenItem 71.9454
## factor(q_dur_distractor):chosenItem 16.5135
## dur_target:factor(condition) 5.1288
## factor(q_dur_distractor):factor(condition) 6.2384
## chosenItem:factor(condition) 14.2757
## dur_target:factor(q_dur_distractor):chosenItem 8.1615
## dur_target:factor(q_dur_distractor):factor(condition) 3.2804
## dur_target:chosenItem:factor(condition) 3.9489
## factor(q_dur_distractor):chosenItem:factor(condition) 3.0576
## dur_target:factor(q_dur_distractor):chosenItem:factor(condition) 4.9020
## Residuals
## Pr(>F)
## dur_target < 2.2e-16 ***
## factor(q_dur_distractor) < 2.2e-16 ***
## chosenItem < 2.2e-16 ***
## factor(condition) < 2.2e-16 ***
## dur_target:factor(q_dur_distractor) < 2.2e-16 ***
## dur_target:chosenItem < 2.2e-16 ***
## factor(q_dur_distractor):chosenItem 1.011e-10 ***
## dur_target:factor(condition) 0.0001048 ***
## factor(q_dur_distractor):factor(condition) 2.189e-13 ***
## chosenItem:factor(condition) 5.406e-14 ***
## dur_target:factor(q_dur_distractor):chosenItem 1.984e-05 ***
## dur_target:factor(q_dur_distractor):factor(condition) 1.632e-05 ***
## dur_target:chosenItem:factor(condition) 0.0013972 **
## factor(q_dur_distractor):chosenItem:factor(condition) 5.616e-05 ***
## dur_target:factor(q_dur_distractor):chosenItem:factor(condition) 1.060e-09 ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ggpredict(f16, terms = c("dur_target", "q_dur_distractor", "chosenItem", "condition")))
first fixation item
dat %>%
group_by(subj, condition, chosenItem, firstFixItem) %>%
summarise(n = n()) %>%
ungroup(subj, condition, chosenItem) %>%
complete(subj, condition, chosenItem) %>%
ggplot(., aes(x = as.numeric(as.character(condition)), y = n, color = firstFixItem)) +
geom_point() + stat_summary(fun.y = "mean", geom = "line") + ggtitle("Number of first fixation")
## `summarise()` has grouped output by 'subj', 'condition', 'chosenItem'. You can
## override using the `.groups` argument.
## Warning: Removed 34 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 34 rows containing missing values (`geom_point()`).
first fixation item (choice considered)
dat %>%
group_by(subj, condition, chosenItem, firstFixItem) %>%
summarise(n = n()) %>%
ungroup(subj, condition, chosenItem) %>%
complete(subj, condition, chosenItem) %>%
ggplot(., aes(x = as.numeric(as.character(condition)), y = n, color = firstFixItem)) +
geom_point() + stat_summary(fun.y = "mean", geom = "line") + ggtitle("Number of first fixation") + facet_wrap(. ~ chosenItem)
## `summarise()` has grouped output by 'subj', 'condition', 'chosenItem'. You can
## override using the `.groups` argument.
## Warning: Removed 34 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 34 rows containing missing values (`geom_point()`).
gaze shift (この三変数を使ってチョイスを予測)
hist(dat$gazeShift_total)
dat %>%
group_by(subj, condition) %>%
summarize(m_gazeShift_target_distractor = mean(gazeShift_target_distractor),
m_gazeShift_distractor_dud = mean(gazeShift_distractor_dud),
m_gazeShift_target_dud = mean(gazeShift_target_dud),
m_gazeShift_total = mean(gazeShift_total)) %>%
ungroup(subj, condition) %>%
complete(subj, condition) %>%
mutate_all(~replace(., is.na(.), 0)) -> gaze_dat
## `summarise()` has grouped output by 'subj'. You can override using the
## `.groups` argument.
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_total)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar")
## Warning: Removed 1 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_distractor)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar")
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_distractor_dud)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar")
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_dud)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar")
gaze shift (choice considered)
dat %>%
group_by(subj, chosenItem, condition) %>%
summarize(m_gazeShift_target_distractor = mean(gazeShift_target_distractor),
m_gazeShift_distractor_dud = mean(gazeShift_distractor_dud),
m_gazeShift_target_dud = mean(gazeShift_target_dud),
m_gazeShift_total = mean(gazeShift_total)) %>%
ungroup(subj, chosenItem, condition) %>%
complete(subj, chosenItem, condition) %>%
mutate_all(~replace(., is.na(.), 0)) -> gaze_dat
## `summarise()` has grouped output by 'subj', 'chosenItem'. You can override
## using the `.groups` argument.
## Warning in `[<-.factor`(`*tmp*`, list, value = 0): 不正な因子水準です。NA
## が発生しました
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_total)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ chosenItem)
## Warning: Removed 6 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 6 rows containing missing values (`geom_point()`).
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_distractor)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ chosenItem)
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_distractor_dud)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ chosenItem)
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_dud)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ chosenItem)
gaze shift (confidence considered)
dat %>%
group_by(subj, conf, condition) %>%
summarize(m_gazeShift_target_distractor = mean(gazeShift_target_distractor),
m_gazeShift_distractor_dud = mean(gazeShift_distractor_dud),
m_gazeShift_target_dud = mean(gazeShift_target_dud),
m_gazeShift_total = mean(gazeShift_total)) %>%
ungroup(subj, conf, condition) %>%
complete(subj, conf, condition) %>%
mutate_all(~replace(., is.na(.), 0)) -> gaze_dat
## `summarise()` has grouped output by 'subj', 'conf'. You can override using the
## `.groups` argument.
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_total)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf)
## Warning: Removed 9 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 9 rows containing missing values (`geom_point()`).
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_distractor)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf)
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_distractor_dud)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf)
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_dud)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf)
gaze shift (choice and confidence considered)
dat %>%
group_by(subj, chosenItem, conf, condition) %>%
summarize(m_gazeShift_target_distractor = mean(gazeShift_target_distractor),
m_gazeShift_distractor_dud = mean(gazeShift_distractor_dud),
m_gazeShift_target_dud = mean(gazeShift_target_dud),
m_gazeShift_total = mean(gazeShift_total)) %>%
ungroup(subj, chosenItem, conf, condition) %>%
complete(subj, chosenItem, conf, condition) %>%
mutate_all(~replace(., is.na(.), 0)) -> gaze_dat
## `summarise()` has grouped output by 'subj', 'chosenItem', 'conf'. You can
## override using the `.groups` argument.
## Warning in `[<-.factor`(`*tmp*`, list, value = 0): 不正な因子水準です。NA
## が発生しました
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_total)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf + chosenItem, nrow = 4)
## Warning: Removed 25 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 25 rows containing missing values (`geom_point()`).
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_distractor)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf + chosenItem, nrow = 4)
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_distractor_dud)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf + chosenItem, nrow = 4)
ggplot(gaze_dat, aes(x = condition, y = m_gazeShift_target_dud)) +
geom_point(position = position_dodge(width = 0.3)) + ylim(0, 2.5) +
stat_summary(fun.y = "mean", geom = "crossbar") + facet_wrap(. ~ conf + chosenItem, nrow = 4)
